Reviewerly/exHarmony huggingface dataset:
Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice

Abstract Necessity and sufficiency are the building blocks of all successful explanations. Yet despite their importance, these notions have been conceptually underdeveloped and inconsistently applied in explainable artificial intelligence (XAI), a fast-growing research area that is so far lacking in firm theoretical foundations. In this article, an expanded version of a paper originally presented at the 37th Conference on Uncertainty in Artificial Intelligence (Watson et al., 2021), we attempt to fill this gap. Building on work in logic, probability, and causality, we establish the central role of necessity and sufficiency in XAI, unifying seemingly disparate methods in a single formal framework. We propose a novel formulation of these concepts, and demonstrate its advantages over leading alternatives. We present a sound and complete algorithm for computing explanatory factors with respect to a given context and set of agentive preferences, allowing users to identify necessary and sufficient conditions for desired outcomes at minimal cost. Experiments on real and simulated data confirm our method’s competitive performance against state of the art XAI tools on a diverse array of tasks.
The role of transparency in multi-stakeholder educational recommendations

Recommender systems have been successfully applied to alleviate the problem of information overload and assist users’ decision makings. Multi-stakeholder recommender systems produce the item recommendations to the end user by considering the perspective of multiple stakeholders. Existing research on multi-stakeholder recommendations relies on the offline evaluations only, and the online studies are still under investigation. This paper made the first attempt to examine the multi-stakeholder recommendations through online studies. On the one hand, we use online user studies to compare different recommendation models. On the other hand, we develop novel user interfaces to enhance the transparency and exploit the role of transparency in multi-stakeholder recommender systems. We collect our own dataset in educational learning and use it as case study to perform the online studies in multi-stakeholder recommendations. We observe that the explanation of the key parameters in the recommendation models can enhance the transparency, which further affects the decision making of different stakeholders.
Privacy-Preserving Flexible Access Control for Encrypted Data in Internet of Things

Along with the development of edge computing and the cloud, the Internet of Things (IoT) is affecting and changing people’s lives. Data sharing has played an important role in the IoT, but the leakage of private user information poses a new security threat to the users. Thus, flexible fine-grained access control for such shared data is proposed in this article as an effective and secure method of eliminating vulnerabilities. However, the disclosure of access policies will also expose users’ private information. Recently, Yang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> attempted to solve this problem and proposed a framework based on attribute-based encryption for shared data onto IEEE IoT-J(DOI: 10.1109/JIOT.2016.2571718). They hide the access policies by using a bloom filter (BF) and attempt to address privacy preservation in IoT. However, we demonstrate several security weaknesses of their framework and point out its vulnerability to dictionary attacks and access policy guessing attacks. Then, an improved IoT solution is proposed. Under this proposal, the attribute values are stored in BF while the attribute names are embedded in the access policy. The proposed scheme can resist dictionary attacks and access policy guessing attacks. In addition, it simultaneously realizes large attribute sets, an efficient decryption algorithm, and adaptive security. Security analysis and performance evaluations show that the presented scheme achieves higher security and implementation simplicity in the IoT than other currently available schemes.
Convolutional neural networks based on fractional-order momentum for parameter training

This paper proposes a parameter training method via the fractional-order momentum for convolutional neural networks (CNNs). To update the parameters of CNNs more smoothly, the parameter training method via the fractional-order momentum is proposed based on the Grünwald-Letnikov (G-L) difference operation. The stochastic classical momentum (SCM) algorithm and adaptive moment (Adam) estimation algorithm are improved by replacing the integer-order difference with the fractional-order difference. Meanwhile, the linear and the nonlinear methods are discussed to adjust the fractional-order. Therefore, the proposed methods can improve the flexibility and the adaptive ability of CNN parameters. We analyze the validity of the methods by using MNIST dataset and CIFAR-10 dataset, and the experimental results show that the proposed methods can improve the recognition accuracy and the learning convergence speed of CNNs compared with the traditional SCM and Adam methods.
WITHDRAWN: Enhanced security in IOT environment using Blockchain: A survey

Blockchain (BC) methods is one of the best technologies in the Internet of Things (IoT) environments, it is a distributed sharing mechanism and communicate the IoT devices with secure manner. A blockchain is a connection of blocks and each block is linked to its earlier blocks. All block has the security hash code, previous block hash, and its data. The transaction between the BlockChain are the data is transfer between one IoT block nodes to another IoT Block node. The IoT device nodes are various types of physical devices like smart devices with embedded sensors, actuators, and capable of be in touch with other IoT device nodes. The responsibility of BlockChain in IoT is to grant a method to provide protected data transmission through IoT device nodes. In this paper discussed about the blockchain technology is how to secure the IoT transmission data and what are the challenges comes into these technologies. BlockChain is a safe method that can be used widely. IoT needs this type of skill to permit protected communication among IoT device nodes in mixed atmosphere.
Online Task Assignment and Coordination in Multi-Robot Fleets

We propose a loosely-coupled framework for integrated task assignment, motion planning, coordination and control of heterogeneous fleets of robots subject to non-cooperative tasks. The approach accounts for the important real-world requirement that tasks can be posted asynchronously. We exploit systematic search for optimal task assignment, where interference is considered as a cost and estimated with knowledge of the kinodynamic models and current state of the robots. Safety is guaranteed by an online coordination algorithm, where the absence of collisions is treated as a hard constraint. The relation between the weight of interference cost in task assignment and computational overhead is analyzed empirically, and the approach is compared against alternative realizations using local search algorithms for task assignment.
DeepFake Videos Detection Based on Texture Features

In recent years, with the rapid development of deep learning technologies, some neural network models have been applied to generate fake media. DeepFakes, a deep learning based forgery technology, can tamper with the face easily and generate fake videos that are difficult to be distinguished by human eyes. The spread of face manipulation videos is very easy to bring fake information. Therefore, it is important to develop effective detection methods to verify the authenticity of the videos. Due to that it is still challenging for current forgery technologies to generate all facial details and the blending operations are used in the forgery process, the texture details of the fake face are insufficient. Therefore, in this paper, a new method is proposed to detect DeepFake videos. Firstly, the texture features are constructed, which are based on the gradient domain, standard deviation, gray level co-occurrence matrix and wavelet transform of the face region. Then, the features are processed by the feature selection method to form a discriminant feature vector, which is finally employed to SVM for classification at the frame level. The experimental results on the mainstream DeepFake datasets demonstrate that the proposed method can achieve ideal performance, proving the effectiveness of the proposed method for DeepFake videos detection.
An Overview of Fuzzy Systems

Fuzzy systems have found widespread application in several contexts and proved their suitability in tackling a number of diverse real-world problems. However, the realization of such systems must be well grounded on some solid theoretical bases that scientists and developers should properly master. In this chapter we discuss the key elements of fuzzy systems, as well as the different instances of their implementation. To this aim, an overview of Fuzzy Set Theory is preliminarily proposed, as an indispensable foundation which is useful to fix the basic concepts, the mathematical underpinning, and the formal notation. In this way, we are able to suitably introduce the concept of fuzzy rule-based systems, illustrating the different components involved in their structure. We review also various types of fuzzy rule-based systems, focusing a special attention on details related to the design of interpretable models.
Task-specific information outperforms surveillance-style big data in predictive analytics

Increasingly, human behavior can be monitored through the collection of data from digital devices revealing information on behaviors and locations. In the context of higher education, a growing number of schools and universities collect data on their students with the purpose of assessing or predicting behaviors and academic performance, and the COVID-19–induced move to online education dramatically increases what can be accumulated in this way, raising concerns about students’ privacy. We focus on academic performance and ask whether predictive performance for a given dataset can be achieved with less privacy-invasive, but more task-specific, data. We draw on a unique dataset on a large student population containing both highly detailed measures of behavior and personality and high-quality third-party reported individual-level administrative data. We find that models estimated using the big behavioral data are indeed able to accurately predict academic performance out of sample. However, models using only low-dimensional and arguably less privacy-invasive administrative data perform considerably better and, importantly, do not improve when we add the high-resolution, privacy-invasive behavioral data. We argue that combining big behavioral data with “ground truth” administrative registry data can ideally allow the identification of privacy-preserving task-specific features that can be employed instead of current indiscriminate troves of behavioral data, with better privacy and better prediction resulting.
The Future of the Transition to E-learning and Distance Learning Using Artificial Intelligence

Now, as you know, research in the world's recent times over the new Corona pandemic has revealed the need for education e-learning, distance learning, and what it is important to keep it moving. Support must be given to this vital and problematic area, which brought all educational institutions from the primary to the university to stop working suddenly and move from traditional to modern it is e-learning and distance learning in which they have used many skills. In this article, we will talk about The problem of how to move to e-learning and distance learning using modern techniques, and the very important thing that COVID-19 has revealed in the use and application of this type of education and the mechanisms to promote it within its educational systems. To properly develop and apply it, artificial intelligence technology, which in turn depends on the well-informed and rigorous analysis of student, teacher, scientific, and educational electronic environment, must be used.
A Joint Model for Named Entity Recognition With Sentence-Level Entity Type Attentions

Named entity recognition (NER) is one fundamental task in natural language processing, which is typically addressed by neural condition random field (CRF) models, regarding the task as a sequence labeling problem. Sentence-level information has been shown positive for the task. Equipped with sophisticated neural structures such as long-short term memory network (LSTM), implicit sentence-level global information can be exploited fully, and has also been demonstrated effective in previous studies. In this work, we propose a new method for better learning of these sentence-level features in an explicit manner. Concretely, we suggest an auxiliary task, namely sentence-level named type prediction (i.e., determining whether a sentence includes a certain kind of named type), to supervise the feature representation learning globally. We conduct experiments on six benchmark datasets of various languages to evaluate our method. The results show that our final model is highly effective, resulting in significant improvements and leading to highly competitive results on all datasets.
Quantum self-supervised learning

Abstract The resurgence of self-supervised learning, whereby a deep learning model generates its own supervisory signal from the data, promises a scalable way to tackle the dramatically increasing size of real-world data sets without human annotation. However, the staggering computational complexity of these methods is such that for state-of-the-art performance, classical hardware requirements represent a significant bottleneck to further progress. Here we take the first steps to understanding whether quantum neural networks (QNNs) could meet the demand for more powerful architectures and test its effectiveness in proof-of-principle hybrid experiments. Interestingly, we observe a numerical advantage for the learning of visual representations using small-scale QNN over equivalently structured classical networks, even when the quantum circuits are sampled with only 100 shots. Furthermore, we apply our best quantum model to classify unseen images on the ibmq_paris quantum computer and find that current noisy devices can already achieve equal accuracy to the equivalent classical model on downstream tasks.
Semi-Supervised Learning in Medical MRI Segmentation: Brain Tissue with White Matter Hyperintensity Segmentation Using FLAIR MRI

White-matter hyperintensity (WMH) is a primary biomarker for small-vessel cerebrovascular disease, Alzheimer's disease (AD), and others. The association of WMH with brain structural changes has also recently been reported. Although fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) provide valuable information about WMH, FLAIR does not provide other normal tissue information. The multi-modal analysis of FLAIR and T1-weighted (T1w) MRI is thus desirable for WMH-related brain aging studies. In clinical settings, however, FLAIR is often the only available modality. In this study, we thus propose a semi-supervised learning method for full brain segmentation using FLAIR. The results of our proposed method were compared with the reference labels, which were obtained by FreeSurfer segmentation on T1w MRI. The relative volume difference between the two sets of results shows that our proposed method has high reliability. We further evaluated our proposed WMH segmentation by comparing the Dice similarity coefficients of the reference and the results of our proposed method. We believe our semi-supervised learning method has a great potential for use for other MRI sequences and will encourage others to perform brain tissue segmentation using MRI modalities other than T1w.
Real-time monitoring implementation of PV/T façade system based on IoT

PV/T façade system is a novel style for solar renewable energy applications, which the system can improve the efficiency of the comprehensive utilization of solar energy. In order to research the performance of PV/T façade system, a modular remote monitoring system based on the Internet of Things and cloud platform technology is proposed, built, deployed as experimental prototype in this paper. The system collects temperature, pressure and wind speed through two RS485 RTU modules, and transmits data to database on the cloud platform via 4G network. The monitoring system allows authorized users to access real-time data or alarm information in a remote way via a website or mobile application. Besides the PV/T System operating parameters, meteorological data are also monitored to ensure a more complete analysis of performance. The proposed modular architecture can be built easily and rapidly, and onsite experimental test shows the stability of the monitoring system, which makes the system flexible enough for potential future modifications.
Haptic-Guided Path Generation for Remote Car-Like Vehicles

Despite significant advances in robot autonomy, manual intervention by a human operator is necessary in many situations. This usually requires qualified staff and some robot-specific input device even for the comparatively simple case of platform locomotion. For this reason, we propose a novel path generation method applicable to car-like vehicles. With this method, the operator “draws” a desired 2D path by walking in a large-scale haptic interface while a guiding force is exerted, which ensures that the generated path can later be accurately followed by a path tracking controller running offline on a remote robot. We present a local optimization-based path planner, a higher-level path generation algorithm utilizing the aforementioned planner, and a force feedback law. Experiments show improved feasibility of the generated paths without affecting the operator's ability to make decisions independently.
A Reconstruction Method for Missing Data in Power System Measurement Based on LSGAN

The integrity of data is an essential basis for analyzing power system operating status based on data. Improper handling of measurement sampling, information transmission, and data storage can lead to data loss, thus destroying the data integrity and hindering data mining. Traditional data imputation methods are suitable for low-latitude, low-missing-rate scenarios. In high-latitude, high-missing-rate scenarios, the applicability of traditional methods is in doubt. This paper proposes a reconstruction method for missing data in power system measurement based on LSGAN (Least Squares Generative Adversarial Networks). The method is designed to train in an unsupervized learning mode, enabling the neural network to automatically learn measurement data, power distribution patterns, and other complex correlations that are difficult to model explicitly. It then optimizes the generator parameters using the constraint relations implied by true sample data, enabling the trained Generator to generate highly accurate data to reconstruct the missing data. The proposed approach is entirely data-driven and does not involve mechanistic modeling. It can still reconstruct the missing data in the case of high latitude and high loss rate. We test the effectiveness of the proposed method by comparing three other GAN derivation methods in our experiments. The experimental results show that the proposed method is feasible and effective, and the accuracy of the reconstructed data is higher while taking into account the computational efficiency.
Rethinking semantic-visual alignment in zero-shot object detection via a softplus margin focal loss

Zero-shot object detection (ZSD) aims to locate and recognize novel objects without additional training samples. Most existing methods usually map visual features to semantic space, resulting in a hubness problem, and learning an effective feature mapping between the two modalities remains a considerable challenge. In this work, we propose a novel end-to-end framework, Semantic-Visual Auto-Encoder (SVAE) network, to tackle the above issues. Distinct from previous works that utilize fully-connected layers to learn the feature mapping, we implement a 1-dimensional convolution with various shared filters to construct the auto-encoder, which maps semantic features to visual space to alleviate the hubness problem. Specifically, we design a novel loss function, Softplus Margin Focal Loss (SMFL), for object classification channel to align the projected semantic features in visual space and address the class imbalance problem. The SMFL improves the discrimination of projections on positive and negative categories and maintains the property of focal loss. Besides, to promote the localization performance for novel objects, we also provide semantic information for object localization channel and utilize a trainable matrix to align the semantic-visual mapping, considering noises in semantic representations. We conduct extensive experiments on four challenging benchmarks. The experimental results show the competitive performances compared with state-of-the-art approaches. Especially, we achieve 8.39%/6.58% mean average precision (mAP) improvements for ZSD/general-ZSD on Microsoft COCO benchmark.
Integration of Knowledge Management in a Virtual Learning Environment: A Systematic Review

In recent years, significant development has ensued confronting e-learning academically and practically. This evolution in information technologies attempts to capitalize on individual intervention and interaction in the learning process to be more efficient and effective, especially in e-learning adopted in virtual communities. The main concern in adopting e-learning in virtual environments is the transfer and acquisition of knowledge in all its aspects. Thus, many tools and frameworks have been developed to solve this issue and to help managers in integrating knowledge management (KM) efficiently and effectively with their learning management systems (LMSs). A systematic literature review was performed from 2005 to 2020 from many databases: Springer, IEEE, Elsevier, and Emerald, where 198 papers were found. After duplications were removed, 94 papers were left, followed by the elimination process, which includes applying inclusion and exclusion criteria. Sixteen papers remained. The main research method used was analyzing and reviewing previous research in these fields. The studies took place in different countries, but the leaders in this field were China, Europe, and Iran. The systematic review mainly highlighted the role of knowledge management processes (KMPs) in virtual learning environments (VLEs), including the concept of e-learning. In addition, it focused on how KM could be integrated successfully in LMS in the VLE. Furthermore, it emphasized the impact of this integration and its advantages on e-learning elements: the learners, the instructors, and the organization. Additionally, it indicated that the most adopted KMP in LMS was knowledge creation. The finding was that KM plays a major role in e-learning in terms of creating a different and distinct education method capable of bringing about change—a Renaissance—in the current era. It also guides thought, experience, and knowledge in the right direction through the use of technology.