Reviewerly/exHarmony huggingface dataset:
Beyond ITQ: Efficient binary multi-view subspace learning for instance retrieval

The existing hashing methods mainly handle either the feature based nearest-neighbor search or the category-level image retrieval, whereas a few efforts are devoted to instance retrieval problem. In this paper, we propose a binary multi-view fusion framework for directly recovering a latent Hamming subspace from the multi-view features for instance retrieval. More specifically, the multi-view subspace reconstruction and the binary quantization are integrated in a unified framework so as to minimize the discrepancy between the original multi-view high-dimensional Euclidean space and the resulting compact Hamming subspace. Besides, our method is essentially an unsupervised learning scheme without any labeled data involved, and thus can be used in the cases when the supervised information is unavailable or insufficient. Experiments on public benchmark and large-scale datasets reveal that our method achieves competitive retrieval performance comparable to the state-of-the-arts and has excellent scalability in large-scale scenario.
Research on Multi-path Network in Cloud Computing Based on SCTP

With the continuous development of the Cloud computing, IOT and the Edge computing, much more pressure was brought to the Internet. At the same time, the higher effective network is needed. Therefore, in order to improve the efficiency of the transport, the current usually used the bonding load balancing techniques based on TCP to increase the transmission bandwidth and improved the transmission efficiency. But the TCP defects, such as head of line blocking and SYN attacks become a bottleneck to the development of the internet. Stream Control Transmission Protocol (SCTP) is a new transfer protocol with multi-streaming and multi-homing properties. It has greatly improved the security and stability of the network service. In this paper, we present a concurrent multi-path transfer and bonding load balancing technology that based on SCTP, then use the SCTP multi-homing properties, as well as the CMT multi-path transfer technology, design a download program, contrast the basic TCP technology and the bonding load balancing technology. The contrast indicates that use the multi-path transfer technology of SCTP with CMT can improve the network efficiency and security.
Design of Intelligent Control and Monitoring System for Agriculture Based on Renewable Energy and IoT

Abstract As the population rises, agriculture is becoming a big growth field worldwide. Improving production and farm efficiency without constant manual supervision to satisfy the rising demand for food is a major challenge in agriculture. Climate change is also a significant issue for the agriculture industry, aside from the growing population. The research aims to provide the Internet of Things (IoT) with an intelligent agriculture system. Intelligent farming that provides high precision crop management, helpful data collection and automatic farming techniques can be used. This work provides an intelligent framework to track ground moisture and temperature in agriculture. Without human interference, appropriate action is taken after analyzing sensed data dependent on these principles. Temperature &amp; relative humidity are assessed and processed for further data analyses in the Thing Speak Database.
Self-Organizing Software Models for the Internet of Things: Complex Software Structures That Emerge Without a Central Controller

The Internet of Things (IoT) envisions the integration of physical objects into software systems for automating crucial aspects of our lives, such as healthcare, security, agriculture, and city management. Although the vision is promising, with the rapid advancement of hardware and communication technologies, IoT systems are becoming increasingly dynamic, large, and complex to the extent that manual management becomes infeasible. Thus, it is of paramount importance to provide software engineering foundations for constructing autonomic IoT systems. In this paper, we introduce a novel paradigm referred to as self-organizing software models in which IoT software systems are not explicitly programmed, but emerge in a decentralized manner during system operation, with minimal or without human intervention. We particularly present an overview of these models by including their definition, motivation, research challenges, and potential directions.
A Pairing-Free Identity-Based Identification Scheme with Tight Security Using Modified-Schnorr Signatures

The security of cryptographic schemes is proven secure by reducing an attacker which breaks the scheme to an algorithm that could be used to solve the underlying hard assumption (e.g., Discrete Logarithm, Decisional Diffie–Hellman). The reduction is considered tight if it results in approximately similar probability bounds to that of solving the underlying hard assumption. Tight security is desirable as it improves security guarantees and allows the use of shorter parameters without the risk of compromising security. In this work, we propose an identity-based identification (IBI) scheme with tight security based on a variant of the Schnorr signature scheme known as TNC signatures. The proposed IBI scheme enjoys shorter parameters and key sizes as compared to existing IBI schemes without increasing the number of operations required for its identification protocol. Our scheme is suitable to be used for lightweight authentication in resource-constrained Wireless Sensor Networks (WSNs) as it utilizes the lowest amount of bandwidth when compared to other state-of-the-art symmetric key lightweight authentication schemes. Although it is costlier than its symmetric key counterparts in terms of operational costs due to its asymmetric key nature, it enjoys other benefits such as decentralized authentication and scalable key management. As a proof of concept to substantiate our claims, we perform an implementation of our scheme to demonstrate its speed and memory usage when it runs on both high and low-end devices.
On the geometry of nearly orthogonal lattices

Nearly orthogonal lattices were formally defined in [4], where their applications to image compression were also discussed. The idea of “near orthogonality” in 2-dimensions goes back to the work of Gauss. In this paper, we focus on well-rounded nearly orthogonal lattices in Rn and investigate their geometric and optimization properties. Specifically, we prove that the sphere packing density function on the space of well-rounded lattices in dimension n≥3 does not have any local maxima on the nearly orthogonal set and has only one local minimum there: at the integer lattice Zn. Further, we show that the nearly orthogonal set cannot contain any perfect lattices for n≥3, although it contains multiple eutactic (and even strongly eutactic) lattices in every dimension. This implies that eutactic lattices, while always critical points of the packing density function, are not necessarily local maxima or minima even among the well-rounded lattices. We also prove that a (weakly) nearly orthogonal lattice in Rn contains no more than 4n−2 minimal vectors (with any smaller even number possible) and establish some bounds on coherence of these lattices.
PoseDet: Fast Multi-Person Pose Estimation Using Pose Embedding

Current methods of multi-person pose estimation typically treat the localization and the association of body joints separately. It is convenient but inefficient, leading to additional computation and a waste of time. This paper, however, presents a novel framework PoseDet (Estimating Pose by Detection) to localize and associate body joints simultaneously at higher inference speed. Moreover, we propose the keypoint-aware pose embedding to represent an object in terms of the locations of its keypoints. The proposed pose embedding contains semantic and geometric information, allowing us to efficiently access discriminative and informative features. It is utilized for candidate classification and body joint localization in PoseDet, leading to robust predictions of various poses. This simple framework achieves an unprecedented speed and a competitive accuracy on the COCO benchmark compared with state-of-the-art methods. Extensive experiments on the CrowdPose benchmark show the robustness in the crowd scenes. Code is available at https://github.com/IIGROUP/PoseDet.
Convolutional neural network-based post-filtering for compressed YUV420 images and video

Images and videos compressed with lossy compression algorithms usually suffer from visible distortions, especially when the bitrate is low. To improve the quality without spending extra bitrate, many image and video codecs have built-in filters to mitigate these artifacts. However, most of them are only applied on the luminance channel, while the chrominance channels remain unmodified. While this is partly justified by the observation that the luminance channel usually contains more details and has higher-resolution than the chrominance channels. We observe that the luminance and chrominance channels still have latent correlations. Therefore, the post-filtering of the chrominance channels is also beneficial and can be driven by the information from the luminance channel. In this paper, we propose a 3-stage YUV post-filtering network for compressed YUV420 images and video. The proposed 3-stage structure not only improves the quality of the luminance channel, but also exploits the luma-chroma correlations to improve the quality of the chrominance channels. Our experimental results show that the proposed approach achieves 3.60%/12.75%/14.93% Bj⊘ntegaard Delta bitrate improvement for the Y, U and V channels over the VVC 10.0 codec for All-Intra configuration.
IoT-based Ambient Parameters Monitoring Device for in-Home Elderly Care

During the last decades, more and more demographic, social and economic changes have influenced the percentage of the ageing population, leading thus to an increasing number of elderly people, correlated with a direct and dramatic influence on the healthcare and social services. Furthermore, the grown longevity of the elderly, the shortage of an appropriate number of caretakers and the raising empowerment of the seniors regarding the self-management of their health status, have led to the stringent necessity of implementing smart environments inside their homes in order to benefit of a prolonged independent, active, healthy and secure life. The Internet of Things (IoT) technology has gained a lot of attention due to its strong potential for supporting these smart environments through devices that can remotely monitor elderly's health and daily activities, without the need to be constantly assisted by a medical specialist. For a comprehensive elderly-targeted remote monitoring able to sustain proactive and preventive measures, the ambient factors should be closely measured and correlated with the physiological and physical vital parameters. RO-SmartAgeing is an undergoing project which aims to create such a smart environment in the home of an elderly person for providing accurate and less-intrusive health and lifestyle monitoring and support services. This paper highlights a component of RO-SmartAgeing project, namely a system that guarantees the collection of ambient-related parameters in order to keep under control the home environment of the patient. Apart from the suggested literature review, as to demonstrate the feasibility of the proposed system, both a proof of concept is used in order to describe the idea and the functionality of the product, and also the conclusions drawn after some preliminary tests have been performed.
ResNet-50 for Classifying Indonesian Batik with Data Augmentation

Batik is composed of various artistic images and patterns, which are called batik motifs. The diversity of batik motifs is influenced by the culture of a region which has a philosophical meaning. Indonesia as a country of cultural diversity has unique batik motifs in each region. Manual identification of batik motifs requires special knowledge and experiences from experts. Various methods are applied to classify images, among others is the Convolutional Neural Network (CNN) method. This study classifies batik images by applying deep learning using the Convolutional Neural Network (CNN) method with ResNet architecture. The number of original batik image dataset consists of 300 images with 50 classes. Augmentation process produce 1200 new image with the same number of classes. Testing scenario compare the accuracy between original data and augmented data with ratio 80:20 for data training and testing. The confusion matrices shows the model provides the highest accuracy performance at 96%.
Detecting anomalous payments networks: A dimensionality-reduction approach

Anomaly-detection methods are aimed at identifying observations that deviate manifestly from what is expected. Such methods are usually run on low-dimensional data, such as time series data. However, the increasing importance of high-dimensional payments and exposure data for financial oversight requires methods for detecting anomalous networks. To detect an anomalous network, dimensionality reduction allows measuring of the extent to which the network's main connective features (i.e. the structure) deviate from those regarded as typical. The key to dimensionality-reduction methods is the ability to reconstruct data with an error; this reconstruction error serves as a yardstick for deviation from what is typical. Principal component analysis (PCA) is used as a dimensionality-reduction method, and a clustering algorithm is used to classify reconstruction errors as normal or anomalous. Based on data from Colombia's large-value payments system and a set of synthetic anomalous networks created through simulations of intraday payments, detecting anomalous payments networks is feasible and promising for financial-oversight purposes.
A framework for ID fraud prevention policies in E-tailing sector

Identity fraud could lead to loss of revenue, causes operational problems to e-tailers and damages the firm's reputation. Most research in this domain focuses on the security technologies or system users’ security compliance related issues. In this research, we direct attention to understanding the content and breadth of identity fraud prevention policies. We conducted thirty-three semi-structured interviews with employees and management of three large UK based e-tailers. It was found that while e-tailers have policies and other related arrangements that address information security and data theft prevention, they often lack policies on identity fraud prevention. Additionally, we found that the design of security policy awareness strategies, approaches to updating security policies, and enforcement of security compliance/audits, do not help prevent identity fraud. Based on this analysis, we developed the Identity Fraud Prevention Framework (IFPF) to help e-tailers develop and implement better identity fraud prevention practices.
Unsupervised Learning of Depth Estimation Based on Attention Model from Monocular Images

At present, the accuracy of unsupervised monocular depth estimation method is not high, the outline is not clear. To solve this problem, we propose a jointly unsupervised learning framework for monocular depth and camera motion estimation from video sequences in this work. Specifically, we introduce an Atrous Spatial Pyramid Pooling (ASPP) module and an attention model. The former module is able to encode multi-scale contextual information by probing the incoming features with filters or pooling operations at multiple rates and multiple effective fields-of-view, while the latter model enables the network to maintain the shape of objects and enhance edges of the depth map. Experiments on KITTI and Cityscapes datasets show that our method can effectively improve the accuracy of the monocular depth estimation, solve the depth estimation boundary blur problem and preserve the details of the depth map.
Reducing Counterfeit Drugs with Blockchains: A Survey

Blockchain (BC) technology has revealed unusual opportunities to reduce fraud in the pharmaceutical, food and electrical equipment's, industries. Specifically, the attractive features of blockchain technology, such immutability, decentralization, distribution, and transparency, can address the issues of trustless between parties, tracking and monitoring all processes of drugs supply chain. Several research studies show that blockchain solutions help to reduce drugs counterfeiting and improve the process of monitoring and tracking all stage of supply chain, a comprehensive survey on this topic is lacking. In this survey, we provide insights into the adoption of blockchain technology to prevent counterfeiting in pharmaceutical industry, thus contributing to the improvement and management of drugs supply chain systems.
What Does It Mean to Explain? A User-Centered Study on AI Explainability

One frequent concern associated with the development of AI models is their perceived lack of transparency. Consequently, the AI academic community has been active in exploring mathematical approaches that can increase the explainability of models. However, ensuring explainability thoroughly in the real world remains an open question. Indeed, besides data scientists, a variety of users is involved in the model lifecycle with varying motivations and backgrounds. In this paper, we sought to better characterize these explanations needs. Specifically, we conducted a user research study within a large institution that routinely develops and deploys AI model. Our analysis led to the identification of five explanation focuses and three standard user profiles that together enable to better describe what explainability means in real life. We also propose a mapping between explanation focuses and a set of existing explainability approaches as a way to link the user view and AI-born techniques.
RamFace: Race Adaptive Margin Based Face Recognition for Racial Bias Mitigation

Recent studies show that there exist significant racial bias among state-of-the-art (SOTA) face recognition algorithms, i.e., the accuracy for Caucasian is consistently higher than that for other races like African and Asian. To mitigate racial bias, we propose the race adaptive margin based face recognition (RamFace) model, designed under the multi-task learning framework with the race classification as the auxiliary task. The experiments show that the race classification task can enforce the model to learn the racial features and thus improve the discriminability of the extracted feature representations. In addition, a racial bias robust loss function, i.e., race adaptive margin loss, is proposed such that different optimal margins can be automatically derived for different races in training the model, which further mitigates the racial bias. The experimental results show that on RFW dataset, our model not only achieves SOTA face recognition accuracy but also mitigates the racial bias problem. Besides, RamFace is also tested on several public face recognition evaluation benchmarks, i.e., LFW, CPLFW and CALFW, and achieves better performance than the commonly used face recognition methods, which justifies the generalization capability of RamFace.
Prediction of Arrhythmia with Machine Learning Algorithms

The present study uses the age, sex, diabetes mellitus, and arrhythmia data of patients from the datasets presented in an existing study to predict arrhythmia with machine learning algorithms, K-Nearest Neighbors (KNN), and Naive Bayes methods. The outputs are schematically presented, and the conclusions related to the Bayes theorem and KNN algorithms are compared. In the case of increasing the value of neighboring k in the KNN method, it is seen that the accuracy rate approaches the result obtained from the Naive Bayes method.
Are mortgage loan closing delay risks predictable? A predictive analysis using text mining on discussion threads

Abstract Loan processors and underwriters at mortgage firms seek to gather substantial supporting documentation to properly understand and model loan risks. In doing so, loan originations become prone to closing delays, risking client dissatisfaction and consequent revenue losses. We collaborate with a large national mortgage firm to examine the extent to which these delays are predictable, using internal discussion threads to prioritize interventions for loans most at risk. Substantial work experience is required to predict delays, and we find that even highly trained employees have difficulty predicting delays by reviewing discussion threads. We develop an array of methods to predict loan delays. We apply four modern out‐of‐the‐box sentiment analysis techniques, two dictionary‐based and two rule‐based, to predict delays. We contrast these approaches with domain‐specific approaches, including firm‐provided keyword searches and “smoke terms” derived using machine learning. Performance varies widely across sentiment approaches; while some sentiment approaches prioritize the top‐ranking records well, performance quickly declines thereafter. The firm‐provided keyword searches perform at the rate of random chance. We observe that the domain‐specific smoke term approaches consistently outperform other approaches and offer better prediction than loan and borrower characteristics. We conclude that text mining solutions would greatly assist mortgage firms in delay prevention.
Effectiveness of System-Facilitated Monitoring Strategies on Learning in an Intelligent Tutoring System

To effectively process complex information within intelligent tutoring systems (ITSs), learners are required to engage in metacognitive monitoring micro-processes (content evaluations [CEs], judgments of learning [JOLs], feelings of knowing [FOKs], and monitoring progress towards goals [MPTGs]). Learners' average monitoring micro-process strategy frequencies were used to examine learning gains using a person-centered approach as they interacted with MetaTutor. Undergraduates (n = 94) engaged in self-initiated and system-facilitated self-regulated learning (SRL) strategies as they studied the human circulatory system with MetaTutor, a hypermedia-based ITS. Using hierarchical clustering, results showed a difference in learning between clusters differing in metacognitive monitoring process usage. Specifically, learners who used both CEs and FOKs for a greater proportion of monitoring strategy usage had significantly greater learning gains than learners who used MPTGs. Implications for monitoring strategy usage across different micro-processes and the development of ITSs to facilitate and scaffold learners' interactions with these micro-processes via prompting are discussed.
Semantic Similarity of Alternatives Fostered by Conversational Negation

Abstract Conversational negation often behaves differently from negation as a logical operator: when rejecting a state of affairs, it does not present all members of the complement set as equally plausible alternatives, but it rather suggests some of them as more plausible than others (e.g., “This is not a dog, it is a wolf/*screwdriver”). Entities that are semantically similar to a negated entity tend to be judged as better alternatives (Kruszewski et al., 2016). In fact, Kruszewski et al. (2016) show that the cosine similarity scores between the distributional semantics representations of a negated noun and its potential alternatives are highly correlated with the negated noun‐alternatives human plausibility ratings. In a series of cloze tasks, we show that negation likewise restricts the production of plausible alternatives to similar entities. Furthermore, completions to negative sentences appear to be even more restricted than completions to an affirmative conjunctive context, hinting at a peculiarity of negation.