Reviewerly/exHarmony huggingface dataset:
Visualization of Twitter Sentiments on Kashmir Territorial Conflict

This paper aims to showcase the sentiments of Twitter users on the Kashmir conflict, which is an unresolved territorial conflict between two countries, India and Pakistan. Sixty thousand (60k) tweets have been collected using popular keywords posted by users living in the two involved countries and conflicting state. We proposed a twofold adaptive approach to exploit and compare different sentiment analysis techniques, namely lexicons (VADER and NRC) and machine learning. The analysis brings out that positive sentiments are higher in both the involved countries. However, there is a belief that people of both countries are negative and vicious about this act. The observations may help to break this myth and to foster positive engagement between the two nations, which are at the edge of a full-fledged war. The study provides a list of top Twitter influencer handles in each country and the disputed state. Thus, the purpose of this research is to scientifically analyze the situation to arrive at a fair decision and thereby removing unjust opinions that cause a lot of disturbance. This research can be used as fundamental to visualize the extensive insights of a critical event and help in identifying the groups which try to incite users on Twitter.
Self-Supervised Regional and Temporal Auxiliary Tasks for Facial Action Unit Recognition

Automatic facial action unit (AU) recognition is a challenging task due to the scarcity of manual annotations. To alleviate this problem, a large amount of efforts has been dedicated to exploiting various methods which leverage numerous unlabeled data. However, many aspects with regard to some unique properties of AUs, such as the regional and relational characteristics, are not sufficiently explored in previous works. Motivated by this, we take the AU properties into consideration and propose two auxiliary AU related tasks to bridge the gap between limited annotations and the model performance in a self-supervised manner via the unlabeled data. Specifically, to enhance the discrimination of regional features with AU relation embedding, we design a task of RoI inpainting to recover the randomly cropped AU patches. Meanwhile, a single image based optical flow estimation task is proposed to leverage the dynamic change of facial muscles and encode the motion information into the global feature representation. Based on these two self-supervised auxiliary tasks, local features, mutual relation and motion cues of AUs are better captured in the backbone network with the proposed regional and temporal based auxiliary task learning (RTATL) framework. Extensive experiments on BP4D and DISFA demonstrate the superiority of our method and new state-of-the-art performances are achieved.
SOoD: Self-Supervised Out-of-Distribution Detection Under Domain Shift for Multi-Class Colorectal Cancer Tissue Types

The goal of out-of-distribution (OoD) detection is to identify unseen categories of inputs different from those seen during training, which is an important requirement for the safe deployment of deep neural networks in computational pathology. Additionally, to make OoD detection applicable in clinical applications, one may encounter image data distribution shifts. This paper argues that practical OoD detection should handle both semantic shift and data distribution shift simultaneously. We propose a new self-supervised OoD detector for colorectal cancer tissue types based on a clustering scheme. Our work’s central tenet benefits from multi-view consistency learning with a supplementary view based on style augmentation to mitigate domain shift. The learned representation is then adapted to minimize images’ predictive entropy to segregate indistribution examples from OoDs on the target data domain. We evaluated our method on two public colorectal tissue types datasets. Our method achieved state-of-the-art OoD detection performance over various self-supervised baselines. The code, data, and models are available at https://github.com/BehzadBozorgtabar/SOoD.
Generalized Pair Weights of Linear Codes and Linear Isomorphisms Preserving Pair Weights

In this paper, we introduce the notion of generalized pair weights of an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[n, k]$ </tex-math></inline-formula> -linear code over the finite field <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {F}_{q}$ </tex-math></inline-formula> and the notion of pair <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> -equiweight codes, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\le r\le k-1$ </tex-math></inline-formula> . Some basic properties of generalized pair weights of linear codes over finite fields are derived. We obtain a necessary and sufficient condition for an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[n,k]$ </tex-math></inline-formula> -linear code to be a pair equiweight code, and we characterize pair <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> -equiweight codes for any <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\le r\le k-1$ </tex-math></inline-formula> . A necessary and sufficient condition for a linear isomorphism to preserve pair weights between two linear codes is obtained. At the end of this paper, an application of generalized pair weights of linear codes to symbol-pair read wire-tap channels of type II is introduced.
A New View of Multisensor Data Fusion: Research on Generalized Fusion

Multisensor data generalized fusion algorithm is a kind of symbolic computing model with multiple application objects based on sensor generalized integration. It is the theoretical basis of numerical fusion. This paper aims to comprehensively review the generalized fusion algorithms of multisensor data. Firstly, the development and definition of multisensor data fusion are analyzed and the definition of multisensor data generalized fusion is given. Secondly, the classification of multisensor data fusion is discussed, and the generalized integration structure of multisensor and its data acquisition and representation are given, abandoning the research characteristics of object oriented. Then, the principle and architecture of multisensor data fusion are analyzed, and a generalized multisensor data fusion model is presented based on the JDL model. Finally, according to the multisensor data generalized fusion architecture, some related theories and methods are reviewed, and the tensor-based multisensor heterogeneous data generalized fusion algorithm is proposed, and the future work is prospected.
Hindsight Intermediate Targets for Mapless Navigation With Deep Reinforcement Learning

Autonomous mapless navigation is very challenging since it is difficult to effectively find the way without a map. In this article, we address a problem encountered when realizing reinforcement learning (RL)-based autonomous mapless navigation of a mobile vehicle with depth sensors when only local depth images and relative target positions are available. This study aims to determine the velocity of the mobile vehicle to avoid collisions and reach the final destination as soon as possible. To accomplish this objective, a new approach, called hindsight-intermediate-target-based RL (HIT-RL), to realize autonomous mapless navigation is proposed to effectively take detour routes to the destination based solely on recent local observations. The proposed HIT-RL generates virtual intermediate targets that guide the mobile vehicle to the final destination. The velocity of the mobile vehicle, as the action of RL, is determined to aim for the intermediate targets, and trained with a neural network to reach the original target by rewarding for it. Navigation tasks realized with an aerial vehicle, or a drone, that the proposed HIT-RL allows the mobile vehicle to effectively escape from dead ends without a map and, hence, reach the final destination within a reasonable time. The navigation performance is also illustrated with an actual mobile vehicle system to show that the proposed HIT-RL exhibits good performance in practical environments.
Robust autocovariance least-squares noise covariance estimation algorithm

Kalman filter (KF) is the main algorithm in the field of optimal estimation. KF can realize the optimal estimation only under the premise that both the function model and the random model are known. In most practical applications, random models are often unknown, and unknown or biased random models will make filtering suboptimal. Besides, the gross errors will cause the innovation to appear abnormal, making the innovation statistics abnormal. The abnormal innovation statistics will lead to the deviation of the estimated noise covariance information. Therefore, when there are gross errors, how to ensure the random model's correct estimation is a major problem faced by the current random model estimation method. To solve this problem, we propose a robust autocovariance least-squares noise covariance estimation algorithm (RALS). In the new method, we first introduce the robust Kalman filtering method (RKF) based on the chi-square test to resist the influence of abnormal innovation on subsequent epoch state estimation; Then, based on the basic idea of the correlation robustness method, we constructed the correlation robust innovation function sequence model, taking into account the correlation between the innovation sequences, to repair the abnormal innovation statistics, so as to obtain accurate post-test innovation statistics; Subsequently, we use the autocovariance least square method to eliminate the coupling effect between the two types of noise covariance information, to correctly estimate the two types of noise covariance information; Finally, we adopt an iterative strategy to eliminate the influence of a priori random model deviation, to realize the separation of the gross error coupled in the abnormal innovation and the prior random model deviation. Two experimental results show that: compared with the KF, RKF, and ALS methods, the new method has higher noise covariance estimation accuracy and filtering accuracy.
BlindOR: an Efficient Lattice-Based Blind Signature Scheme from OR-Proofs

An OR-proof is a protocol that enables a user to prove the possession of a witness for one of two (or more) statements, without revealing which one. Abe and Okamoto (CRYPTO 2000) used this technique to build a partially blind signature scheme whose security is based on the hardness of the discrete logarithm problem. Inspired by their approach, we present Open image in new window , an efficient blind signature scheme from OR-proofs based on lattices over modules. Using OR-proofs allows us to reduce the security of our scheme from the \(\mathsf {MLWE}\) and \(\mathsf {MSIS}\) problems, yielding a much more efficient solution compared to previous works.
How statically-typed functional programmers write code

How working statically-typed functional programmers write code is largely understudied. And yet, a better understanding of developer practices could pave the way for the design of more useful and usable tooling, more ergonomic languages, and more effective on-ramps into programming communities. The goal of this work is to address this knowledge gap: to better understand the high-level authoring patterns that statically-typed functional programmers employ. We conducted a grounded theory analysis of 30 programming sessions of practicing statically-typed functional programmers, 15 of which also included a semi-structured interview. The theory we developed gives insight into how the specific affordances of statically-typed functional programming affect domain modeling, type construction, focusing techniques, exploratory and reasoning strategies, and expressions of intent. We conducted a set of quantitative lab experiments to validate our findings, including that statically-typed functional programmers often iterate between editing types and expressions, that they often run their compiler on code even when they know it will not successfully compile, and that they make textual program edits that reliably signal future edits that they intend to make. Lastly, we outline the implications of our findings for language and tool design. The success of this approach in revealing program authorship patterns suggests that the same methodology could be used to study other understudied programmer populations.
Searchable Encryption With Autonomous Path Delegation Function and Its Application in Healthcare Cloud

Outsourcing medical data to healthcare cloud has become a popular trend. Since medical data of patients contain sensitive personal information, they should be encrypted before outsourcing. However, information retrieval methods based on plaintext cannot be directly applied to encrypted data. In this article, we present a new cryptographic primitive named conjunctive keyword search with secure channel free and autonomous path delegation function (AP-SCF-PECKS), which can be applied in scenarios where patients want to search for and autonomous delegate their private medical information without revealing their private key. Particularly, the proposed solution allows patients to set up multi-hop delegation path with their preferences, and the delegated doctors in the path can search for and access the patient’s private medical information with priority from high to low. Patients can ensure that authorized doctors are always trustworthy, and unauthorized users cannot obtain the private medical information of patients. Moreover, the scheme supports the conjunctive keyword search, secure channel free, and is secure against chosen keyword attack, chosen ciphertext attack, and keyword guessing attack. The security of proposed scheme has been formally proved in the standard model. Finally, the performance evaluations demonstrate that the overhead of proposed scheme are modest for healthcare cloud scenarios.
HandLock: Enabling 2-FA for Smart Home Voice Assistants using Inaudible Acoustic Signal

The use of voice-control technology has become mainstream and is growing worldwide. While voice assistants provide convenience through automation and control of home appliances, the open nature of the voice channel makes voice assistants difficult to secure. As a result voice assistants have been shown to be vulnerable to replay attacks, impersonation attacks and inaudible voice commands. Existing defenses do not provide a practical solution as they either rely on external hardware (e.g., motion sensors) or work under very constrained settings (e.g., holding the device close to a user’s mouth). We introduce the concept of using a gesture-based authentication system for smart home voice assistants called HandLock, which uses built-in microphones and speakers to generate and sense inaudible acoustic signals to detect the presence of a known (i.e., authorized) hand gesture. Our proposed approach can act as a second-factor authentication (2-FA) for performing specific sensitive operations like confirming online purchases through voice assistants. Through extensive experiments involving 45 participants, we show that HandLock can achieve on average 96.51% true-positive-rate (TPR) at the expense of 0.82% false-acceptance-rate (FAR). We perform a comprehensive analysis of HandLock under various settings to showcase its accuracy, stability, resilience to attacks, and usability. Our analysis shows that HandLock can not only successfully thwart impersonation attacks, but can do so while incurring very low overheads and is compatible with modern voice assistants.
Improved SiamRPN++ with Clustering-Based Frame Differencing for Object Tracking of Remote Sensing Videos

Deep learning (DL) based object tracking methods have achieved encouraging results on natural videos. However, directly applying these DL-based methods to the vehicle tracking of optical remote sensing videos (ORSV) still faces many challenges. Different with the vehicles in nature videos, most vehicles in ORSV are blurry, small in size, and highly similar to other vehicles. Furthermore, blurred vehicles easily blend into the background and are difficult to distinguish. To solve these problems, an improved Siamrpn++ with clustering-based frame differencing (CFD-SiamRPN++) is proposed. In CFD-SiamRPN++, a clustering method is used to divide the differencing map among adjacent frames into two clusters. By using the statistics of the clustering results, each cluster is judged to the target or not. According to the judgment of each cluster, the differencing map is refined by reducing the background noises and retaining moving information of vehicles. Then, the refined differencing map is fused with the original frame as the input of the tracking network to enhance the discriminative ability of small-sized and blurred vehicles. In the tracking phase, SiamRPN++ is selected as the tracking network due to its feature extraction capability with multi-scale feature fusion and efficient tracking performance. Experiment results based on Jilin-1 ORSV dataset show that the proposed method provides a competitive tracking performance over state-of art deep learning methods.
Why don’t people use character-level machine translation?

We present a literature and empirical survey that critically assesses the state of the art in character-level modeling for machine translation (MT).Despite evidence in the literature that character-level systems are comparable with subword systems, they are virtually never used in competitive setups in WMT competitions.We empirically show that even with recent modeling innovations in characterlevel natural language processing, characterlevel MT systems still struggle to match their subword-based counterparts.Character-level MT systems show neither better domain robustness, nor better morphological generalization, despite being often so motivated.However, we are able to show robustness towards source side noise and that translation quality does not degrade with increasing beam size at decoding time.
A Multi-attention Collaborative Deep Learning Approach for Blood Pressure Prediction

We develop a deep learning model based on Long Short-term Memory (LSTM) to predict blood pressure based on a unique data set collected from physical examination centers capturing comprehensive multi-year physical examination and lab results. In the Multi-attention Collaborative Deep Learning model (MAC-LSTM) we developed for this type of data, we incorporate three types of attention to generate more explainable and accurate results. In addition, we leverage information from similar users to enhance the predictive power of the model due to the challenges with short examination history. Our model significantly reduces predictive errors compared to several state-of-the-art baseline models. Experimental results not only demonstrate our model’s superiority but also provide us with new insights about factors influencing blood pressure. Our data is collected in a natural setting instead of a setting designed specifically to study blood pressure, and the physical examination items used to predict blood pressure are common items included in regular physical examinations for all the users. Therefore, our blood pressure prediction results can be easily used in an alert system for patients and doctors to plan prevention or intervention. The same approach can be used to predict other health-related indexes such as BMI.
General Fuzzy C-Means Clustering Strategy: Using Objective Function to Control Fuzziness of Clustering Results

As one of the most commonly used clustering methods, the fuzzy C-means (FCM) clustering strategy extends the notion of hard clustering to associate each pattern with every cluster using a membership function. Although a lot of efforts have been made by the clustering community, it is still unclear how to evaluate the fuzziness of different versions of FCM. To fill this theoretical blank, by observing a family of objective functions, a definition of fuzzy degree is provided to quantify the fuzziness of different versions of FCM according to their dedicated objective functions. Then, a general fuzzy C-means (GFCM) clustering algorithm is proposed to solve the clustering problem of using FCM under different distance metrics and fuzzy degrees. From the perspective of fuzzy attribute, the using of this fuzzy degree can be used to reveal the essential difference among a group of FCM-based clustering algorithms, and the proposed GFCM achieves the aim of using objective functions to control the fuzziness of clustering results. Additionally, some properties and relations of these FCM-based clustering algorithms under different fuzzy degrees are discussed and the convergence and stability of the proposed GFCM are proved. Finally, extensive experiments have been performed to demonstrate that by comparison with the effect caused by distance metric, the choices of fuzzy degree have a more significant effect and improvement on the performances of a FCM-based clustering algorithm.
FAIR Ontologies for Transparent and Accountable AI: A Hospital Adverse Incidents Vocabulary Case Study

In this paper, the relation between the FAIR (Findable, Accessible, Interoperable, Reusable) ontologies and accountability and transparency of ontology-based AI systems is analysed. Also, governance-related gaps in ontology quality evaluation metrics were identified by examining their relation with FAIR principles and FAcct (Fairness, Accountability, Transparency) governance aspects. A simple SKOS vocabulary, titled "Hospital Adverse Incidents Classification Scheme" (HAICS) has been used as a use case for this study. Theoretically, we found that there is a straight relation between FAIR principles and FAccT AI, which means that FAIR ontologies enhance transparency and accountability in ontology-based AI systems. We suggest that "FAIRness" should be assessed as one of the ontology quality evaluation aspects.
DORA: Towards policy optimization for task-oriented dialogue system with efficient context

Recently, reinforcement learning (RL) has been applied to task-oriented dialogue systems by using latent actions to solve shortcomings of supervised learning (SL). In this paper, we propose a multi-domain task-oriented dialogue system, called Dialogue System with Optimizing a Recurrent Action Policy using Efficient Context (DORA), that uses SL, with subsequently applied RL to optimize dialogue systems using a recurrent dialogue policy. This dialogue policy recurrently generates explicit system actions as a both word-level and high-level policy. As a result, DORA is clearly optimized during both SL and RL steps by using an explicit system action policy that considers an efficient context instead of the entire dialogue history. The system actions are both interpretable and controllable, whereas the latent actions are not. DORA improved the success rate by 6.6 points on MultiWOZ 2.0 and by 10.9 points on MultiWOZ 2.1.