Reviewerly/exHarmony huggingface dataset:
AMEX AI Labs at SemEval-2022 Task 10: Contextualized fine-tuning of BERT for Structured Sentiment Analysis

We describe the work carried out by AMEX AI Labs on the structured sentiment analysis task at SemEval-2022.This task focuses on extracting fine grained information w.r.t. to source, target and polar expressions in a given text.We propose a BERT based encoder, which utilizes a novel concatenation mechanism for combining syntactic and pretrained embeddings with BERT embeddings.Our system achieved an average rank of 14/32 systems, based on the average scores across seven datasets for five languages provided for the monolingual task.The proposed BERT based approaches outperformed BiLSTM based approaches used for structured sentiment extraction problem.We provide an in-depth analysis based on our post submission analysis.
Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients

Discovering the underlying mathematical expressions describing a dataset is a core challenge for artificial intelligence. This is the problem of $\textit{symbolic regression}$. Despite recent advances in training neural networks to solve complex tasks, deep learning approaches to symbolic regression are underexplored. We propose a framework that leverages deep learning for symbolic regression via a simple idea: use a large model to search the space of small models. Specifically, we use a recurrent neural network to emit a distribution over tractable mathematical expressions and employ a novel risk-seeking policy gradient to train the network to generate better-fitting expressions. Our algorithm outperforms several baseline methods (including Eureqa, the gold standard for symbolic regression) in its ability to exactly recover symbolic expressions on a series of benchmark problems, both with and without added noise. More broadly, our contributions include a framework that can be applied to optimize hierarchical, variable-length objects under a black-box performance metric, with the ability to incorporate constraints in situ, and a risk-seeking policy gradient formulation that optimizes for best-case performance instead of expected performance.
EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators

The recognition of emotion and dialogue acts enriches conversational analysis and help to build natural dialogue systems. Emotion interpretation makes us understand feelings and dialogue acts reflect the intentions and performative functions in the utterances. However, most of the textual and multi-modal conversational emotion corpora contain only emotion labels but not dialogue acts. To address this problem, we propose to use a pool of various recurrent neural models trained on a dialogue act corpus, with and without context. These neural models annotate the emotion corpora with dialogue act labels, and an ensemble annotator extracts the final dialogue act label. We annotated two accessible multi-modal emotion corpora: IEMOCAP and MELD. We analyzed the co-occurrence of emotion and dialogue act labels and discovered specific relations. For example, Accept/Agree dialogue acts often occur with the Joy emotion, Apology with Sadness, and Thanking with Joy. We make the Emotional Dialogue Acts (EDA) corpus publicly available to the research community for further study and analysis.
EVOLUTION AND ANALYSIS OF SECURED HASH ALGORITHM (SHA) FAMILY

With the rapid advancement of technologies and proliferation of intelligent devices, connecting to the internet challenges have grown manifold, such as ensuring communication security and keeping user credentials secret. Data integrity and user privacy have become crucial concerns in any ecosystem of advanced and interconnected communications. Cryptographic hash functions have been extensively employed to ensure data integrity in insecure environments. Hash functions are also combined with digital signatures to offer identity verification mechanisms and non-repudiation services. The federal organization National Institute of Standards and Technology (NIST) established the SHA to provide security and optimal performance over some time. The most well-known hashing standards are SHA-1, SHA-2, and SHA-3. This paper discusses the background of hashing, followed by elaborating on the evolution of the SHA family. The main goal is to present a comparative analysis of these hashing standards and focus on their security strength, performance and limitations against common attacks. The complete assessment was carried out using statistical analysis, performance analysis and extensive fault analysis over a defined test environment. The study outcome showcases the issues of SHA-1 besides exploring the security benefits of all the dominant variants of SHA-2 and SHA-3. The study also concludes that SHA-3 is the best option to mitigate novice intruders while allowing better performance cost-effectively.
Students’ Perceptions on the Use of Mobile-Assisted Language Learning (MALL) in Learning Pronunciation

The objectives of this research are to analyze the students’ perceptions of the use of Mobile Assisted Language Learning (MALL) in pronunciation learning and to analyze the problems faced by the students regarding the use of the MALL in pronunciation. Additionally, this research was conducted using a mixed method design. The data for this research were taken from 15 students in the English Education Study Program, at Tadulako University. In collecting the data for this research, the researcher used questionnaires and interviews with the students. The questionnaire was in the form of a statement and it included five degrees of answer options. The interview, on the other hand, was in the form of semi-structured and included 10 items. Then, after analyzing the data obtained, it was concluded that the general perception of the students is that the perception of the students on the use of MALL in pronunciation is positive as they perceive that MALL is effective and beneficial for their pronunciation learning. Further, the students explained that there is one major problem they face regarding the use of MALL in pronunciation that deals with internet connectivity. However, as can be inferred from the students’ explanation, there are three more problems that cause difficulties to students. They are the students’ phones performance; the possibility of the students being redirected; and the students’ familiarity with using mobile phones for learning.
A Credal Least Undefined Stable Semantics for Probabilistic Logic Programs and Probabilistic Argumentation

We present an approach to probabilistic logic programming and probabilistic argumentation that combines elements of the L-stable semantics and the credal semantics. We derive the complexity of inferences, propose an extended version of argumentation graphs with a semantics that maps to the L- stable semantics, and introduce a definition for the probability of an argument.
Pushing Optimal ABox Repair from EL Towards More Expressive Horn-DLs

Ontologies based on Description Logic (DL) represent general background knowledge in a terminology (TBox) and the actual data in an ABox. DL systems can then be used to compute consequences (such as answers to certain queries) from an ontology consisting of a TBox and an ABox. Since both human-made and machine-learned data sets may contain errors, which manifest themselves as unintuitive or obviously incorrect consequences, repairing DL-based ontologies in the sense of removing such unwanted consequences is an important topic in DL research. Most of the repair approaches described in the literature produce repairs that are not optimal, in the sense that they do not guarantee that only a minimal set of consequences is removed. In a series of papers, we have developed an approach for computing optimal repairs, starting with the restricted setting of an EL instance store, extending this to the more general setting of a quantified ABox (where some individuals may be anonymous), and then adding a static EL TBox. Here, we extend the expressivity of the underlying DL considerably, by adding nominals, inverse roles, regular role inclusions and the bottom concept to EL, which yields a fragment of the well-known DL Horn-SROIQ. The ideas underlying our repair approach still apply to this DL, though several non-trivial extensions are needed to deal with the new constructors and axioms. The developed repair approach can also be used to treat unwanted consequences expressed by certain conjunctive queries or regular path queries, and to handle Horn-ALCOI TBoxes with regular role inclusions.
Inference with System W Satisfies Syntax Splitting

In this paper, we investigate inductive inference with system W from conditional belief bases with respect to syntax splitting. The concept of syntax splitting for inductive inference states that inferences about independent parts of the signature should not affect each other. This was captured in work by Kern-Isberner, Beierle, and Brewka in the form of postulates for inductive inference operators expressing syntax splitting as a combination of relevance and independence; it was also shown that c-inference fulfils syntax splitting, while system P inference and system Z both fail to satisfy it. System W is a recently introduced inference system for nonmonotonic reasoning that captures and properly extends system Z as well as c-inference. We show that system W fulfils the syntax splitting postulates for inductive inference operators by showing that it satisfies the required properties of relevance and independence. This makes system W another inference operator besides c-inference that fully complies with syntax splitting, while in contrast to c-inference, also extending rational closure.
Autonomous Robots for Deep Mask-Wearing Detection in Educational Settings during Pandemics

The COVID-19 pandemic has severely impacted various aspects of life, where countries closed their borders, and workplaces and educational institutions shut down their premises in response to lockdowns. This has adversely affected the lives of everyone, including millions of students worldwide, socially, mentally, and physically. Governments and educational authorities worldwide have taken preventive measures, such as social distancing and mask wearing, to control the spread of the virus. This paper proposes an AI-powered autonomous robot for deep mask-wearing detection to enforce proper mask wearing in educational settings. The system includes (1) Simultaneous Localization and Mapping framework to map and navigate the environment (i.e., laboratories and classrooms), (2) a multiclass face mask detection software, and (3) an auditory system to identify and alert improper or no mask wearing. We train our face mask detector using MobileNetV2 architecture and YOLOv2 object detector classification. The results demonstrate that our robot can navigate an educational environment while avoiding obstacles to detect violations. The proposed face mask detection and classification subsystem achieved a 91.4% average precision when tested on students in an engineering laboratory environment.
Exploring the Impact of Temporal Bias in Point-of-Interest Recommendation

Recommending appropriate travel destinations to consumers based on contextual information such as their check-in time and location is a primary objective of Point-of-Interest (POI) recommender systems. However, the issue of contextual bias (i.e., how much consumers prefer one situation over another) has received little attention from the research community. This paper examines the effect of temporal bias, defined as the difference between users' check-in hours, leisure vs.~work hours, on the consumer-side fairness of context-aware recommendation algorithms. We believe that eliminating this type of temporal (and geographical) bias might contribute to a drop in traffic-related air pollution, noting that rush-hour traffic may be more congested. To surface effective POI recommendations, we evaluated the sensitivity of state-of-the-art context-aware models to the temporal bias contained in users' check-in activities on two POI datasets, namely Gowalla and Yelp. The findings show that the examined context-aware recommendation models prefer one group of users over another based on the time of check-in and that this preference persists even when users have the same amount of interactions.
CI-Net: Appearance-Based Gaze Estimation via Cooperative Network

Facial occlusion and different appearances of both eyes in natural scenes can affect the accuracy of gaze estimation based on appearance. Therefore, this paper proposes a gaze estimation model based on cooperative network: CI-Net, including a consistency estimation network (C-Net) and inconsistency estimation network (I-Net). C-Net is used to estimate the Main gaze of the true gaze, and an attention mechanism is added to adaptively assign the weight between eyes and face features. The I-Net is used to estimate the Residual residuals based on true gaze. In addition, Cross attention module is designed in this paper, through which I-Net can selectively obtain information from C-Net, to obtain more accurate eyes directions. The experimental results in this paper show that the CI-Net gain lower angle errors than the current mainstream CNN methods under the condition of different appearance of both eyes and facial occlusion.
Impact of Artificial Intelligence, Robotics, and Automation on Employment

Machine learning is the ability of machines to learn from past experiences using historical data (supervised and semisupervised cases) to solve a given problem. Machines make decisions using Artificial Intelligence (past experiences). Companies adequately understand that increasing business efficiency and employee productivity are of supreme importance to thriving in a highly competitive digital environment. Any process can be automated as long as there is a clear operating procedure available. In today’s time, almost every sector is automating things to minimise cost and improve reliability. So, we need something that will resolve this. In this paper, we will study the many good and bad impacts of artificial intelligence, robotics, and automation on employment. We will also discuss some ideas to minimise the bad impact and maximise the good impact in the related fields
A Review of CPS Design and Vulnerability Analysis

Cyber-Physical Systems (CPS) offer a wide array of applications. Integration of various heterogeneous infrastructures that generate data for intelligent analysis is discussed. The objective of this article is to review CPS and provide a discussion of the system's security flaws. Many earlier investigations have also been properly explained. Because of the increasing usage of CPS in sensitive organizations (e.g., healthcare and connected homes), the requirement for a risk assessment strategy is essential. The major concentration of risk evaluation has shifted from computer risk evaluation to network-based risk analysis as a result of our substantial dependence on the Internet. The goal of CPS vulnerability analysis is to develop a quantitative model for future system protection.
A Deep Learning Powered System to Lie Detection While Online Study

Many education facilities have recently switched to online learning due to the COVID-19 pandemic. The nature of online learning makes it easier for dishonest behaviors, such as cheating or lying during lessons. We propose a new artificial intelligence - powered solution to help educators solve this rising problem for a fairer learning environment. We created a visual representation contrastive learning method with the MobileNetV2 network as the backbone to improve predictability from an unlabeled dataset which can be deployed on low power consumption devices. The experiment shows an accuracy of up to 59%, better than several previous research, proving the usability of this approach.
Quantum Randomness in Cryptography—A Survey of Cryptosystems, RNG-Based Ciphers, and QRNGs

Cryptography is the study and practice of secure communication with digital data and focuses on confidentiality, integrity, and authentication. Random number generators (RNGs) generate random numbers to enhance security. Even though the cryptographic algorithms are public and their strength depends on the keys, cryptoanalysis of encrypted ciphers can significantly contribute to the unveiling of the cipher’s key. Therefore, to ensure high data security over a network, researchers need to improve the randomness of keys as they develop cryptosystems. Quantum particles have a leading edge in advancing RNG technology as they can provide true randomness, unlike pseudo-random numbers generators (PRNGs). In order to increase the level of the security of cryptographic systems based on random numbers, this survey focuses on three objectives: Cryptosystems with related cryptographic attacks, RNG-based cryptosystems, and the design of quantum random number generators (QRNGs). This survey aims to provide researchers with information about the importance of RNG-based ciphers and various research techniques for QRNGs that can incorporate quantum-based true randomness in cryptosystems.
Evaluation of Machine Learning Models for Breast Cancer Diagnosis Via Histogram of Oriented Gradients Method and Histopathology Images

Breast cancer is the main death rate from malignant growth worldwide and the most frequently diagnosed type of cancer in females. Machine learning systems have been developed to assist in the accurate detection of cancer. There are numerous methods for cancer detection. But histopathological images are thought to be more precise. In this study, we used the HOG features extractor to extract statistical features from histopathology images of invasive ductal carcinoma. We chose the following images at random from the histopathology images: 100, 200, 400, 1000, and 2000. These statistical features were then used to train several algorithms, including the decision tree, quadratic discriminant analysis, extra randomized trees, gradient boosting, gaussian process classifier, naive bayes, nearest centroid, multilayer perceptron, and support vector machine, to identify whether or not the images depict cancerous or noncancerous growth. The algorithms' performance was evaluated depending on the specificity, accuracy, sensitivity, precision, F1_score, and AUC. The algorithms used worked best when the number of images was set to 100. As the number of images went up, their effectiveness went down.
Mirror Ritual

Mirror Ritual is an interactive installation that challenges the existing paradigms in our understanding of human emotion and machine perception. In contrast to prescriptive interfaces, the work's real-time affective interface engages the audience in the iterative conceptualisation of their emotional state through the use of affectively-charged machine generated poetry. The audience are encouraged to make sense of the mirror's poetry by framing it with respect to their recent life experiences, effectively 'putting into words' their felt emotion. This process of affect labelling and contextualisation works to not only regulate emotion, but helps to construct the rich personal narratives that constitute human identity.
Inference in High-Dimensional Linear Regression via Lattice Basis Reduction and Integer Relation Detection

We focus on the high-dimensional linear regression problem, where the algorithmic goal is to efficiently infer an unknown feature vector $\beta^*\in\mathbb{R}^p$ from its linear measurements, using a small number $n$ of samples. Unlike most of the literature, we make no sparsity assumption on $\beta^*$, but instead adopt a different regularization: In the noiseless setting, we assume $\beta^*$ consists of entries, which are either rational numbers with a common denominator $Q\in\mathbb{Z}^+$ (referred to as $Q$-rationality); or irrational numbers supported on a rationally independent set of bounded cardinality, known to learner; collectively called as the mixed-support assumption. Using a novel combination of the PSLQ integer relation detection, and LLL lattice basis reduction algorithms, we propose a polynomial-time algorithm which provably recovers a $\beta^*\in\mathbb{R}^p$ enjoying the mixed-support assumption, from its linear measurements $Y=X\beta^*\in\mathbb{R}^n$ for a large class of distributions for the random entries of $X$, even with one measurement $(n=1)$. In the noisy setting, we propose a polynomial-time, lattice-based algorithm, which recovers a $\beta^*\in\mathbb{R}^p$ enjoying $Q$-rationality, from its noisy measurements $Y=X\beta^*+W\in\mathbb{R}^n$, even with a single sample $(n=1)$. We further establish for large $Q$, and normal noise, this algorithm tolerates information-theoretically optimal level of noise. We then apply these ideas to develop a polynomial-time, single-sample algorithm for the phase retrieval problem. Our methods address the single-sample $(n=1)$ regime, where the sparsity-based methods such as LASSO and Basis Pursuit are known to fail. Furthermore, our results also reveal an algorithmic connection between the high-dimensional linear regression problem, and the integer relation detection, randomized subset-sum, and shortest vector problems.
IntersectGAN

Generative adversarial networks (GANs) have demonstrated great success in generating various visual content. However, images generated by existing GANs are often of attributes (e.g., smiling expression) learned from one image domain. As a result, generating images of multiple attributes requires many real samples possessing multiple attributes which are very resource expensive to be collected. In this paper, we propose a novel GAN, namely IntersectGAN, to learn multiple attributes from different image domains through an intersecting architecture. For example, given two image domains $X_1$ and $X_2$ with certain attributes, the intersection $X_1 \cap X_2$ denotes a new domain where images possess the attributes from both $X_1$ and $X_2$ domains. The proposed IntersectGAN consists of two discriminators $D_1$ and $D_2$ to distinguish between generated and real samples of different domains, and three generators where the intersection generator is trained against both discriminators. And an overall adversarial loss function is defined over three generators. As a result, our proposed IntersectGAN can be trained on multiple domains of which each presents one specific attribute, and eventually eliminates the need of real sample images simultaneously possessing multiple attributes. By using the CelebFaces Attributes dataset, our proposed IntersectGAN is able to produce high quality face images possessing multiple attributes (e.g., a face with black hair and a smiling expression). Both qualitative and quantitative evaluations are conducted to compare our proposed IntersectGAN with other baseline methods. Besides, several different applications of IntersectGAN have been explored with promising results.