Reviewerly/exHarmony huggingface dataset:
Double Recurrent Dense Network for Single Image Deraining

Rain streaks can affect visual visibility, and hence disable many visual algorithms. So we present a double recurrent dense network for removing rain streaks from single image. Assume the rain image is the superposition of the clean image and the rain streaks, we directly learn the rain streaks from the rainy image. In contrast to other models, we introduce a double recurrent scheme to promote better information reuse of rain streaks and relative clean image. For rain streaks, the LSTM cascaded by DenseNet blocks is used as the basic model. The relative clean image predicted by subtracting the rain streaks from the rainy image is then input to the basic model in an iterative way. Benefiting from double recurrent schemes, our model makes full use of rain streaks and image detail information and thoroughly removes rain streaks. Furthermore, we adopt a mix of $L_{1}$ loss, $L_{2}$ loss and SSIM loss to guarantee good rain removing performance. We conduct a plenty of experiments on synthetic and real rainy images, even on similar denoise task, the results not only show our model significantly outperforms the state-of-art methods for removing rain streaks, but also exhibit our model has a high effectiveness for similar task, i.e. image denoising.
Committing to Quantum Resistance, Better: A Speed-and-Risk-Configurable Defence for Bitcoin Against a Fast Quantum Computing Attack

In light of the emerging threat of powerful quantum computers appearing in the near future, we investigate the potential attacks on Bitcoin available to a quantum-capable adversary. In particular, we illustrate how Shor’s quantum algorithm can be used to forge ECDSA based signatures, allowing attackers to hijack transactions. We then propose a simple commit–delay–reveal protocol, which allows users to securely move their funds from non-quantum-resistant outputs to those adhering to a quantum-resistant digital signature scheme. In a previous paper (Stewart et al. R. Soc. Open Sci. 5(6), 180410 (2018)) [1] we presented a similar scheme with a long fixed delay. Here we improve on our previous work, by allowing each user to choose their preferred delay–long for a low risk of attack, or short if a higher risk is acceptable to that user. As before, our scheme requires modifications to the Bitcoin protocol, but once again these can be implemented as a soft fork.
Design of Attribute Based Authenticated Group Key Agreement Protocol Without Pairing

The paper present a multi-party attribute based authenticated key agreement protocol without using bilinear pairing. Attribute based key agreement is needed in verity of applications wherever a fine grained access control is required like design of access control for cloud storage, military applications, broadcast encryption etc. Such applications usually need to establish, a secure session among the participants without knowing their actual identities for subsequent cryptographic (symmetric key) operations. Instead of identities, the participants are authenticates based on a descriptive set of attributes (called as access policy). As per the literature-survey the protocols based on attribute based authentication presented so far requires bilinear pairing computations. Since the implementation of pairing is much costlier than ordinary scalar point multiplication over elliptic curve. Therefore pairing free protocols over attribute-based authentication is much desirable especially in resource constraint network. The present paper employs elliptic curve arithmetic along with Lagrange interpolation instead of bilinear pairing computations. The authentication process of the protocol is information theoretically secure while the confidentiality of the shared key relies on the intractability of Elliptic Curve Discrete Logarithm Problem (ECDLP).
Use of Qualitative Research to Generate a Function for Finding the Unit Cost of Software Test Cases

In this article, we demonstrate a novel use of case research to generate an empirical function through qualitative generalization. This innovative technique applies interpretive case analysis to the problem of defining and generalizing an empirical cost function for test cases through qualitative interaction with an industry cohort of subject matter experts involved in software testing at leading technology companies. While the technique is fully generalizable, this article demonstrates this technique with an example taken from the important field of software testing. The huge amount of software development conducted in today's world makes taking its cost into account imperative. While software testing is a critical aspect of the software development process, little attention has been paid to the cost of testing code, and specifically to the cost of test cases, in comparison to the cost of developing code. Our research fills the gap by providing a function for estimating the cost of test cases.
An efficient pedestrian detection network on mobile GPU with millisecond scale

With a certain number of architectural improvements in deep networks, the real-time object detection on mobile devices especially for human beings has gradually become the basis of many downstream visual applications, such as segmentation, tracking, or high-level object-based reasoning. Therefore, there is an urgent need for the object detection model to have higher performance than a standard real-time benchmark. In our study, we proposed a new light-weighted model with a self-designed feature extractor that is based on Resnet18, and using special anchor and loss scheme to eliminate small-sized persons, according to the practical need of subsequent applications to only detect large-sized persons. At the same time, we put forward a newly-designed weighted-NMS method to remove the highly redundant bounding boxes on the same person. The final result outperforms other tiny networks, for example modified ShuffleNet and PeleeNet with nearly the same speed of 5 ms, by achieving 43.3% mAP on person category of COCO-2017 dataset on MI8 GPU with at least 90% in detection accuracy.
Document Clustering and Topic Modeling: A Unified Bayesian Probabilistic Perspective

Document clustering and topic modeling are fundamental tasks in text mining, that can be unified to reciprocally enhance each other. In this paper, we present a machine learning approach to the joint modeling and interdependent fulfilment of both tasks. In particular, document clustering and topic modeling are seamlessly interrelated under an innovative Bayesian generative model of clusters, topics and contents in text corpora. Such a model assumes that text corpora result from a generative process, in which clusters and topics act as connected latent factors. Essentially, clusters are initially associated with descriptive and actionable topic distributions, that enforce cluster coherence. The individual documents are then assigned to one respective cluster and worded accordingly. Under the devised model, document clustering and topic modeling can be simultaneously performed in an interdependent manner simply by Bayesian reasoning. For this purpose, the mathematical details regarding collapsed Gibbs sampling as well as parameter estimation are derived and implemented into an approximate inference algorithm. Comparative experiments on standard benchmark text corpora reveal the effectiveness of our approach at jointly clustering text documents and unveiling their semantics in terms of coherent topics.
Latent Structure Matching for Knowledge Transfer in Reinforcement Learning

Reinforcement learning algorithms usually require a large number of empirical samples and give rise to a slow convergence in practical applications. One solution is to introduce transfer learning: Knowledge from well-learned source tasks can be reused to reduce sample request and accelerate the learning of target tasks. However, if an unmatched source task is selected, it will slow down or even disrupt the learning procedure. Therefore, it is very important for knowledge transfer to select appropriate source tasks that have a high degree of matching with target tasks. In this paper, a novel task matching algorithm is proposed to derive the latent structures of value functions of tasks, and align the structures for similarity estimation. Through the latent structure matching, the highly-matched source tasks are selected effectively, from which knowledge is then transferred to give action advice, and improve exploration strategies of the target tasks. Experiments are conducted on the simulated navigation environment and the mountain car environment. The results illustrate the significant performance gain of the improved exploration strategy, compared with traditional ϵ -greedy exploration strategy. A theoretical proof is also given to verify the improvement of the exploration strategy based on latent structure matching.
SeLENet: A Semi-Supervised Low Light Face Enhancement Method for Mobile Face Unlock

Facial recognition is becoming a standard feature on new smartphones. However, the face unlocking feature of devices using regular 2D camera sensors exhibits poor performance in low light environments. In this paper, we propose a semi-supervised low light face enhancement method to improve face verification performance on low light face images. The proposed method is a network with two components: decomposition and reconstruction. The decomposition component splits an input low light face image into face normals and face albedo, while the reconstruction component enhances and reconstructs the lighting condition of the input image using the spherical harmonic lighting coefficients of a direct ambient white light. The network is trained in a semi-supervised manner using both labeled synthetic data and unlabeled real data. Qualitative results demonstrate that the proposed method produces more realistic images than the state-of-the-art low light enhancement algorithms. Quantitative experiments confirm the effectiveness of our low light face enhancement method for face verification. By applying the proposed method, the gap of verification accuracy between extreme low light and neutral light face images is reduced from approximately 3% to 0.5%.
Human-Activity Recognition with Smartphone Sensors

The aim of the Human-Activity Recognition (HAR) is to identify the actions carried out by an individual given a data set of parameters recorded by sensors. Successful HAR research has focused on the recognition of relatively simple activities, as sitting or walking and its applications are mainly useful in the fields of healthcare, tele-immersion or fitness tracking. One of the most affordable ways to recognize human activities is to make use of smartphones. This paper draws a comparison line between several ways of processing and training the data provided by smartphone sensors, in order to achieve an accurate score when recognizing the user’s activity.
Enhancing and simplifying data security and privacy for multitiered applications

While databases provide capabilities to enforce security and privacy policies, two major issues still prevent applications from safely delegating such policies to the database. The first one is the loss of user identity in multitiered environments which renders the database security features of little to no value. The second issue is the unsafe coexistence between the security capabilities and fundamental database tenets which creates data leakage vulnerabilities. This paper proposes extensions to database systems to allow applications, such as those used in managing the operations of energy clouds, to safely delegate the security and privacy policies to the database. This delegation reduces complexity for applications and improves overall data security and privacy. Our performance evaluation shows that almost all the TPC-H queries perform the same or better when the security policy is enforced by the database. For the set of queries that performed better, the improvement observed ranges from 8 to 68%.
Machine Learning Usage in Facebook, Twitter and Google Along with the Other Tools

The trend in the current industry and academics is machine learning (ML), and it is not hype but the reality. The market requirements are not limited by the earlier computing and storage models if those can be integrated with ML algorithms. The requirements of current industry, research and other domains like banking, finance, retail and medical have to depend on the collection of huge amounts of the data and analyse the data to better serve the stakeholders. The organizations have to focus on better storage models and advance logics of ML to meet the current needs of the people and usage of the limited amount of time to handle the huge amounts of the data. The ML research is moving in a high potential with the involvement of the social media like Facebook, Twitter and Google. These are not only using the ML perspective in their applications but also contributing to the development of new algorithms and API in the context of big data, ML and deep learning. The paper objective is to walk through the ML, big data along with deep learning fundamentals with the specification of various algorithms in the reference of above-mentioned social media with their contributions in the development of ML landscape along with the API (TensorFlow), services like priority inbox and the algorithms like RankBrain. The discussion will help the researchers and academicians so as to get the overview and detailed significance of ML research and the importance of ML in various dimensions. All these algorithms, tools and services are indirectly dependent on the artificial intelligence (AI) to frame the rules and to fine-tune the rules as per the requirements.
An Efficient Quantum Key Management Scheme

The quantum key distribution playing the role of trusted carrier to augment the symmetric key distribution and is competing with public key cryptography in case of reliability and security. It is secure against unlimited computing power. In the implementation front of quantum key distribution, there is an intersection of fields like quantum physics, optics, computer science and electronics make it more complex. In this paper, we proposed Quantum key distribution technique in which traditional cryptographic techniques are not needed for mutually authenticating the keys. We proposed the base distribution center as a third party, in which both communicating parties share their bases before communication and mutually authenticate each other by transmitting a series of bits and deduce the encryption and decryption keys.
Online Adversarial Planning in μRTS : A Survey

Online planning is an important research area focusing on the problem of real-time decision making, using information extracted from the environment. The aim is to compute, at each decision point, the best decision possible that contributes to the realization of a fixed objective. Relevant application domains include robotics, control engineering and computer games. Real-time strategy (RTS) games pose considerable challenges to artificial intelligence techniques, due to their dynamic, complex and adversarial aspects, where online planning plays a prominent role. They also constitute an ideal research platform and test-bed for online planning. μRTS is an open-source AI research platform that features a minimalistic, yet complete RTS implementation, used by AI researchers for developing and testing intelligent RTS game-playing agents. The unique characteristics of μRTS helped for the emergence of interesting online adversarial planning techniques, dealing with multiple levels of abstraction. This paper presents the major μRTS online planning approaches to date, categorized by the degree of abstraction, in fully and partially observable environments.
Quantum Computing: A Brief Study

This study is specifically about how advancement in physics lead to the exponential growth of computing power and capability. The progress in quantum computing might lead to a computing capability extremely superior to the existing computing performance. Moreover, the performance, massive data processing power, and extremely high computational speed of the quantum computer might also become boon for programmers, scientists and technologists.
Comparing the Effectiveness of the Improved ARLSTem Algorithm with Existing Arabic Light Stemmers

As known in the literature, light stemmers produce more under-stemming errors, while root stemmers produce more over-stemming errors. In this investigation, we deal with the Arabic light stemming problem, where we propose an improvement to ARLSTem algorithm (i.e. ARLSTem v1.1). In particular, we introduce new rules to correct some under-stemming errors produced by ARLSTem. In addition, we compare the new version of ARLSTem with five existing stemming algorithms using ARASTEM corpus. The latter has been corrected, where we have found some errors in seven samples. The experimental results showed that ARLSTem v1.1 outperforms the other existing algorithms in terms of under-stemming and over-stemming errors. Moreover, it presents interesting performances in the Arabic text categorization task.
Multi-classfication Sentiment Analysis Based on the Fused Model

The traditional methods of sentiment classification usually see the data has positive and negative two kinds of attitudes only. But actually, the real data has multi-category sentiment, is positive, negative, neutral and not mentioned four classes. Therefore, when using common classifying methods to analyzing the data sentiment, if the number of few class data is too scarce, it is difficult to learn useful information from them and the final classifying result will tend to most classes. In order to obtain accurate classification results, this paper proposes a multi-classification method based on the combination of Bert (Bidirectional Encoder Representation from Transformers) model and Liblinear (A Library for Large Linear Classification) model (It is abbreviated as B-Liblinear). Due to the Bert model's breakthrough in data preprocessing, this paper prepressed training data set, and obtained the word vector and sentence vectors from data. Next, combined with attribute label and sentiment tendency data, the unstructured data was converted into a structured training data set. It was as the standard input data of Liblinear model to construct a classification model. This model's classification mechanism is "one vs. rest", it can effectively solve the heavy class imbalance problem of massive data in multiple classification tasks. In this paper, the classification result of B-Liblinear model was compared with several classical multi-classification methods. And the experimental results show that the combination of Bert model and Liblinear of dealing with the text multi-classification problem is more accurate.
Digital sketching. Aesthetic practices and technological entanglements

This article explores the ways aesthetic practices and emerging digital technologies are entangled in the practices of industrial designers. Despite the increased interest in the socio-materiality of aesthetic practices, the conception of the materiality and historicity of digital artefacts remains an open issue. It is suggested that the concept of operational form provides an integrative perspective on the technical development and practical enactment of digital technologies. Drawing on an ethnographic case study on the appropriation of a digital sketching application, it is shown how the aesthetic practices and qualities of the digital technologies are reflexively coupled. The study points to the fact that digital technologies are inherently reductive in that they have to abstract from the particularities and richness of concrete aesthetic practices.
Gesture Sequence Matching Algorithm based on FastEDR

The gesture sequence recognition method based on the traditional Dynamic Time Warping(DTW) algorithm will lead to too long calculation time when the sample data sequence length is too large, and the accuracy of the recognition result will be obviously low due to the influence of noise. The FastDTW algorithm is mainly used for DTW acceleration, but it cannot suppress noise, and its time complexity is O(N). Edit Distance on Real sequence(EDR) algorithm can only suppress noise, but its efficiency is not improved, and its time complexity is O(N2). Based on the above problems, FastEDR algorithm is proposed for the matching verification of gesture sequences. The results show that the computational efficiency of FastEDR algorithm is significantly improved compared with DTW algorithm, the error acceptance rate FAR is reduced by 2.94%, and the error rejection rate FRR is reduced by 4.5%. The gesture sequence recognition based on FastEDR algorithm can achieve a better recognition state. If it is applied to unlock mobile phones, it is expected to become a new generation of unlocking method.
Towards Efficient Data Sampling for Temporal Anomaly Detection in Sensor Networks

Data streams sampling is intended to build a sample on which the future data analysis tasks will be performed. Several parameters affect the effectiveness of the built sample: the used sampling algorithm, chosen sampling rate, and window size if the sliding window model is adopted. Thus, given a stream of items, the most challenging task is to select the most relevant sampling technique to apply and the right parameters to employ to sample the data. In this paper, we address the impact of data sampling on the anomaly detection results. First, we develop a new version of the Weighted Random Sampling (WRS) algorithm that samples the data based on their values with respect to the values of their neighbors in the current sliding window. Thereafter, we study the impact of the sampling process on the anomalies detection using the Exponential Weighted Moving Average (EWMA) algorithm. In this context, the comparison of the sampling algorithms is based on their response time in case of anomaly and the relevance of the detected anomalies.
Usability Evaluation of Mobile-based Application for Al-Quran Writing Learning with Gamification

Technological progress is currently running so rapidly, its development has been felt by all levels of society. For example, current technological developments have actually made it easier for us to learn to read and write Al-Quran. Proven applications have emerged such as digital Al-Quran, then applications to learn to recognize and write hijayah letters. In the conventional learning method, almost all teachers teach writing Al-Quran sentence with conventional instructional media by using a white board. We proposed a mobile application that can help students to learn how to write Al-Quran sentence in attractive way by using their finger. Since this application still in prototype stage, further study and analysis of the usability of this application are necessary to validate the feasibility in real implementation. Usability testing using USE questionnaire had been conducted to find out if application can be accepted by users. We give 30 questions about usability to 5 respondents that are familiar with android application. The result show that, the proposed application is acceptable to users in term of Usefulness, ease of use, ease of learning and satisfaction with the score of percentage is 95.36%.