Reviewerly/exHarmony huggingface dataset:
Progressive Distribution Alignment Based on Label Correction for Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) aims to transfer knowledge between different domains. Most of the existing UDA methods try to align the conditional distribution between the source and target domains by utilizing the information of pseudo labels induced from the target domain. To tackle the negative transfer caused by inaccurate pseudo labels, we propose a novel UDA method named progressive distribution alignment based on label correction (PDALC). Specifically, PDALC uses the class discriminative information to perform subspace learning to obtain the domain invariance subspace. Furthermore, a new mechanism of pseudo label correction is introduced to measure the reliability of pseudo labels and utmostly correct the inaccurate pseudo labels. By combining subspace learning with label correction, the performance of PDALC can be continuously improved, which in turn reduces the generation of inaccurate pseudo labels. The experimental results show that the proposed method outperforms the state-of-the-art UDA methods.
Non-interactive Distributional Indistinguishability (NIDI) and Non-malleable Commitments

We introduce non-interactive distributionally indistinguishable arguments (NIDI) to address a significant weakness of NIWI proofs: namely, the lack of meaningful secrecy when proving statements about $$\mathsf {NP}$$ languages with unique witnesses. NIDI arguments allow a prover $${\mathcal P} $$ to send a single message to verifier $${\mathcal V} $$ , from which $${\mathcal V} $$ obtains a sample d from a (secret) distribution $${\mathcal D} $$ , together with a proof of membership of d in an NP language $${\mathcal L} $$ . The soundness guarantee is that if the sample d obtained by the verifier $${\mathcal V} $$ is not in $${\mathcal L} $$ , then $${\mathcal V} $$ outputs $$\bot $$ . The privacy guarantee is that secrets about the distribution remain hidden: for every pair of (sufficiently) hard-to-distinguish distributions $${\mathcal D} _0$$ and $${\mathcal D} _1$$ with support in NP language $${\mathcal L} $$ , a NIDI that outputs samples from $${\mathcal D} _0$$ with proofs of membership in $${\mathcal L} $$ is indistinguishable from one that outputs samples from $${\mathcal D} _1$$ with proofs of membership in $${\mathcal L} $$ .
An Adaptive Ant Colony Algorithm for Autonomous Vehicles Global Path Planning

In order to improve the robustness of the autonomous vehicle path planning algorithm and reduce the number of turns in the planned path, this paper proposes an adaptive ant colony algorithm path planning method. The algorithm optimizes the initial pheromone matrix based on the environment map, reduces the blindness of the initial ant colony in pathfinding, and improves the convergence speed. Then an adaptive heuristic function is used, which adaptively adjusts according to the different proportions of the heuristic function in the algorithm process, so as to avoid the algorithm being trapped in local optimum. The pheromone is updated according to the corners of the planned route, reducing the acute angle of the route and unnecessary turns to further optimize the route. The simulation results show that the proposed algorithm achieves good results. The simulation results show that the improved adaptive ant colony algorithm has faster convergence speed, higher path planning quality, and improved stability of planned paths than classical ant colony algorithms and other adaptive ant colony algorithms.
An Efficient Methodology for Avoiding Threats in Smart Homes with Low Power Consumption in IoT Environment Using Blockchain Technology

The Internet of Things (IoT), a modern age of digital wireless sensor network tools, provides a broad variety of applications in different fields, including security, medical, home control, remote surveillance, etc. Owing to their extensive use and recent large-scale DDoS (distributed denial-of-service) attacks utilizing millions of such devices, the protection of these devices has become an essential thing to tackle. Blockchain technology is combined with IoT gadgets to provide improved protection. As data is exchanged between IoT gadgets, the proposed model is built to create a block after each interaction that is used for deeper analysis and is often used to identify attacks in IoT gadgets. Smart home corresponds to a regular home, with the exception that it includes integrated devices and equipment that provide additional support, power, and comfort for occupants. People are therefore given the opportunity to regulate their own energy efficiency, to have additional safety checks on their homes and many other advantages. Any malicious action that has taken place in the network can be easily tracked with the help of blocks generated using blockchain technology. The concept is evolving, especially in the last few years with the overall technological development. Together with the large benefits of smart homes, there are certain risks that come along and are difficult to predict as the concept is new and evolving. As a consequence, historical data is not adequate or does not exist. The aim of this proposed work is to analyze existing risk assessment methods that can be used to evaluate the cyber risks associated with smart homes and to further analyze them from three perspectives: person, society, and government. The new model is very good for connected home implementations because the degree of protection is increased relative to standard versions.
Short-term Photovoltaic Power Prediction Based on Daily Feature Matrix and Deep Neural Network

In order to reduce the error of short-term photovoltaic (PV) power forecast without irradiance data, a prediction model based on daily feature matrix and long short term-memory (LSTM) deep neural network is proposed. Firstly, various factors affecting PV output are analyzed to select model inputs effectively. On this basis, a new similar day selection method considering the internal and external factors under multi-source data integration scenarios is introduced. Based on weather forecast information and day-ahead PV power data, daily feature matrices can be constructed to determine similar days by calculating the distances between the matrices. Then, the similar historical PV power vector is used as an input of a LSTM deep neural network, combined with meteorological forecast information to realize the final power prediction. Finally, the feasibility of the proposed method can be validated with the actual data of residential PV systems in North America.
Utility Preserved Facial Image De‐identification Using Appearance Subspace Decomposition

Automated human facial image de-identification is a much-needed technology for privacy-preserving social media and intelligent surveillance applications. We propose a novel utility preserved facial image de-identification to subtly tinker the appearance of facial images to achieve facial anonymity by creating “averaged identity faces”. This approach is able to preserve the utility of the facial images while achieving the goal of privacy protection. We explore a decomposition of an Active appearance model (AAM) face space by using subspace learning where the loss can be modeled as the difference between two trace ratio items, and each respectively models the level of discriminativeness on identity and utility. Finally, the face space is decomposed into subspaces that are respectively sensitive to face identity and face utility. For the subspace most relevant to face identity, a k-anonymity de-identification procedure is applied. To verify the performance of the proposed facial image de-identification approach, we evaluate the created “averaged faces” using the extended Cohn-Kanade Dataset (CK+). The experimental results show that our proposed approach is satisfied to preserve the utility of the original image while defying face identity recognition.
MIST: Multiple Instance Spatial Transformer

We propose a deep network that can be trained to tackle image reconstruction and classification problems that involve detection of multiple object instances, without any supervision regarding their whereabouts. The network learns to extract the most significant K patches, and feeds these patches to a task-specific network – e.g., auto-encoder or classifier – to solve a domain specific problem. The challenge in training such a network is the non-differentiable top-K selection process. To address this issue, we lift the training optimization problem by treating the result of top-K selection as a slack variable, resulting in a simple, yet effective, multi-stage training. Our method is able to learn to detect recurring structures in the training dataset by learning to reconstruct images. It can also learn to localize structures when only knowledge on the occurrence of the object is provided, and in doing so it outperforms the state-of-the-art. Code available at https://github.com/ubc-vision/mist
Way to Go! Effects of Motivational Support and Agents on Reducing Foreign Language Anxiety

Using a tutoring system for English as a foreign language, we studied the impact on students' anxiety levels of an animated agent that provides motivational, supportive feedback. We compared two types of feedback — explanatory and motivational supportive feedback — presented in three ways: by text, by voice, or by a character agent. Results showed that using an agent that gives motivational, supportive feedback decreases the learners' anxiety levels overall. We also found that performance and gender interact with the effectiveness of the treatment for reducing foreign language anxiety (FLA). Our findings have implications for promoting equity and determining how best to improve positive emotions and reduce anxiety for all students.
FinSBD-2021: The 3rd Shared Task on Structure Boundary Detection in Unstructured Text in the Financial Domain

Document processing is a foundational pre-processing task in natural language application applied in the financial domain. In this paper, we present the result of FinSBD-3, the 3rd shared task on Structure Boundary Detection in unstructured text in the financial domain. The shared task is organized as part of the 1st Workshop on Financial Technology on the Web. Participants were asked to create system detecting the boundaries of elements in unstructured text extracted from financial PDF. This edition extends the previous shared tasks by adding boundaries of visual elements such as tables, figures, page headers and page footers; on top of sentences, lists and list items which were already present in previous edition of the shared tasks.
IRS: A Large Naturalistic Indoor Robotics Stereo Dataset to Train Deep Models for Disparity and Surface Normal Estimation

Indoor robotics applications heavily rely on scene understanding and reconstruction. Compared to monocular vision, stereo vision methods are more promising to produce accurate geometrical information, such as surface normal and depth/disparity. Besides, deep learning models have shown their superior performance in stereo vision tasks. However, existing stereo datasets rarely contain high-quality surface normal and disparity ground truth, hardly satisfying the demand of training a prospective deep model. To this end, we introduce a large-scale indoor robotics stereo (IRS) dataset with over 100K stereo images and high-quality surface normal and disparity maps. Leveraging the advanced techniques of our customized rendering engine, the dataset is considerably close to the real-world scenes. Besides, we present DTN-Net, a two-stage deep model for surface normal estimation. Extensive experiments show the advantages and effectiveness of IRS in training deep models for disparity estimation, and DTN-Net provides state-of-the-art results for normal estimation compared to existing methods.
Trusted Player Transfer Evaluation for Sport Markets Based on Blockchain and Locality-Sensitive Hashing

With the wide popularity of various sport items and the continuous prosperity of sport business, player transfer events are becoming more and more frequent, which gives birth to a big player transfer market with attractive attentions of spectators. However, for the purposes of business competition or concerns of privacy disclosure, the multiple parties involved in an identical player transfer event are often reluctant to release the concrete transfer fees of a player as the transfer fee is always sensitive enough for the player himself or herself as well as the involved multiple sport clubs. The hidden player transfer fees block the fair and transparent sport business trades and cheat prospective spectators to some extent, which decreases the spectator trust towards player market value and finally, lowers the spectators' satisfaction towards sport business. In this situation, it is becoming a necessity to develop a trusted player transfer evaluation method supported by all the parties involved in player transfer events. Inspired by this challenging task, a trusted player transfer evaluation method with privacy protection is put forward in this paper, which is mainly based on Locality-Sensitive Hashing (LSH) and Blockchain technologies and named TPTE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LSH+B</sub> . At last, a series of simulated experiments are deployed to validate the feasibility of our proposed TPTE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LSH+B</sub> algorithm. Experimental results report that TPTE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LSH+B</sub> has a good evaluation performance with competitive approaches.
$$\textsf {LogStack}$$: Stacked Garbling with $$O(b \log b)$$ Computation

Secure two party computation (2PC) of arbitrary programs can be efficiently achieved using garbled circuits (GC). Until recently, it was widely believed that a GC proportional to the entire program, including parts of the program that are entirely discarded due to conditional branching, must be transmitted over a network. Recent work shows that this belief is false, and that communication proportional only to the longest program execution path suffices (Heath and Kolesnikov, CRYPTO 20, [HK20a]). Although this recent work reduces needed communication, it increases computation. For a conditional with b branches, the players use $$O(b^2)$$ computation (traditional GC uses only O(b)). Our scheme $$\textsf {LogStack}$$ reduces stacked garbling computation from $$O(b^2)$$ to $$O(b \log b)$$ with no increase in communication over [HK20a]. The cause of [HK20a]'s increased computation is the oblivious collection of garbage labels that emerge during the evaluation of inactive branches. Garbage is collected by a multiplexer that is costly to generate. At a high level, we redesign stacking and garbage collection to avoid quadratic scaling. Our construction is also more space efficient: [HK20a] algorithms require O(b) space, while ours use only $$O(\log b)$$ space. This space efficiency allows even modest setups to handle large numbers of branches. [HK20a] assumes a random oracle (RO). We track the source of this need, formalize a simple and natural added assumption on the base garbling scheme, and remove reliance on RO: $$\textsf {LogStack}$$ is secure in the standard model. Nevertheless, $$\textsf {LogStack}$$ can be instantiated with typical GC tricks based on non-standard assumptions, such as free XOR and half-gates, and hence can be implemented with high efficiency. We implemented $$\textsf {LogStack}$$ (in the RO model, based on half-gates garbling) and report performance. In terms of wall-clock time and for fewer than 16 branches, our performance is comparable to [HK20a]'s; for larger branching factors, our approach clearly outperforms [HK20a]. For example, given 1024 branches, our approach is $$31{\times }$$ faster.
Learning Low-Rank Graph With Enhanced Supervision

In this paper, we propose a new semi-supervised graph construction method, which is capable of adaptively learning the similarity relationship between data samples by fully exploiting the potential of pairwise constraints, a kind of weakly supervisory information. Specifically, to adaptively learn the similarity relationship, we linearly approximate each sample with others under the regularization of the low-rankness of the matrix formed by the approximation coefficient vectors of all the samples. In the meanwhile, by taking advantage of the underlying local geometric structure of data samples that is empirically obtained, we enhance the dissimilarity information of the available pairwise constraints via propagation. We seamlessly combine the two adversarial learning processes to achieve mutual guidance. We cast our method as a constrained optimization problem and provide an efficient alternating iterative algorithm to solve it. Experimental results on five commonly-used benchmark datasets demonstrate that our method produces much higher classification accuracy than state-of-the-art methods, while running faster.
Sentiment Analysis of COVID-19 on Saudi Trending Hashtags Using Recurrent Neural Network

The past decade has witnessed rapid development in microblogging platforms such as Twitter, Facebook, and Instagram. They are one of the most powerful sources for information and news due to the tremendous amount of data generated by them. These platforms are full of thoughts, emotions, and feedbacks related to many real-life events; COVID-19 pandemic for instance. With the COVID-19 pandemic, many individuals including media and government agencies use microblogging platforms to share news and opinions regarding the COVID-19 crisis. This has led to a considerable amount of research focus on identifying the hidden knowledge for decision-making in government and corporate organizations. In this paper, sentiment analysis of Arabic tweets using recurrent neural network (RNN) has been carried out on the tweets gathered from 9th to 11th April 2020, that is during the early stage of the pandemic. RNNs have been applied to sentiment analysis, however, they were never used for Arabic sentiment analysis of COVID-19. In this research, we investigate the performance of RNN, particularly Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) in classifying Arabic sentiment analysis regarding COVID-19 crisis. The results indicate the superiority of GRU over LSTM, with an 81% precision, using Global Vector (GloVe) as a word embedding technique.
Spline Interpolation and Deep Neural Networks as Feature Extractors for Signature Verification Purposes

Digital security in modern systems very often uses biometric, and increasingly, new implementations appear. Such applications can be found everywhere, even when picking up the package from courier, we certify its receipt through our signature on the tablet. However, verification of this form is not one of the simplest elements in information processing systems. Given the different sizes, angles, or writing conditions that may affect its stability, new methods to evaluate signatures are constantly needed. In this article, we propose the use of spline interpolation and two types of artificial neural networks to verify the identity of a person based on selected local and global features extracted from the image of a signature. Global features are extracted concerning interpolation and graphic processing methods, while local features are verified using convolutional neural networks. Both sets of features are used in the identity verification process. The article presents the model of the operation together with experiments, taking into account various parameters of the proposed extraction. We have reached an accuracy of 87.7% on the SVC2004 database.
Structural Weakening and Paradoxes

Recently, several authors have pointed out that substructural logics are adequate for developing naive theories that represent semantic concepts such as truth. Among them, three proposals have been explored: dropping cut, dropping contraction and dropping reflexivity. However, nowhere in the substructural literature has anyone proposed rejecting the structural rule of weakening, while accepting the other rules. Some theorists have even argued that this task was not possible, since weakening plays no role in the derivation of semantic paradoxes. In this article, I introduce a theory for naive truth based on the logic resulting from dropping the rule of weakening from classical logic, and maintaining the other structural rules.
GALGOBOT – The College Companion Chatbot

College Campuses are huge in terms of the area they cover. If a certain person has a query that he wants to enquire, he/she would have to travel to various departments gathering segments of the answer to the query he had. The proposed system, GALGOBOT eases the query solving process by saving time and effort. GALGOBOT, a chatbot system acts as a companion and can be integrated on college websites. A Login and Signup System webpages was also added in the system to prevent unknown users from gathering inside information. The software would also ask various questions to get precise answers to the query by utilizing Natural Language Processing (NLP) model which is helpful in the process. GALGOBOT intends to streamline the process of query solving.
Diverse video captioning through latent variable expansion

Automatically describing video content with text description is challenging but important task, which has been attracting a lot of attention in computer vision community. Previous works mainly strive for the accuracy of the generated sentences, while ignoring the sentences diversity, which is inconsistent with human behavior. In this paper, we aim to caption each video with multiple descriptions and propose a novel framework. Concretely, for a given video, the intermediate latent variables of conventional encode-decode process are utilized as input to the conditional generative adversarial network (CGAN) with the purpose of generating diverse sentences. We adopt different Convolutional Neural Networks (CNNs) as our generator that produces descriptions conditioned on latent variables and discriminator that assesses the quality of generated sentences. Simultaneously, a novel DCE metric is designed to assess the diverse captions. We evaluate our method on the benchmark datasets, where it demonstrates its ability to generate diverse descriptions and achieves superior results against other state-of-the-art methods.
Fedns: Improving Federated Learning for Collaborative Image Classification on Mobile Clients

Federated Learning (FL) is a paradigm that aims to support loosely connected clients in learning a global model collaboratively with the help of a centralized server. The most popular FL algorithm is Federated Averaging (FedAvg), which is based on taking weighted average of the client models, with the weights determined largely based on dataset sizes at the clients. In this paper, we propose a new approach, termed Federated Node Selection (FedNS), for the server’s global model aggregation in the FL setting. FedNS filters and re-weights the clients’ models at the node/kernel level, hence leading to a potentially better global model by fusing the best components of the clients. Using collaborative image classification as an example, we show with experiments from multiple datasets and networks that FedNS can consistently achieve improved performance over FedAvg.