Reviewerly/exHarmony huggingface dataset:
Hard-instance learning for quantum adiabatic prime factorization

Prime factorization is a difficult problem with classical computing, whose exponential hardness is the foundation of Rivest-Shamir-Adleman cryptography. With programable quantum devices, adiabatic quantum computing has been proposed as a plausible approach to solve prime factorization, having promising advantage over classical computing. Here, we find there are certain hard instances that are consistently intractable for both classical simulated annealing and unconfigured adiabatic quantum computing (AQC). Aiming at an automated architecture for optimal configuration of quantum adiabatic factorization, we apply a deep reinforcement learning (RL) method to configure the AQC algorithm. By setting the success probability of the worst-case problem instances as the reward to RL, we show the AQC performance on the hard instances is dramatically improved by RL configuration. The success probability also becomes more evenly distributed over different problem instances, meaning the configured AQC is more stable as compared to the unconfigured case. Through a technique of transfer learning, we find prominent evidence that the framework of AQC configuration is scalable---the configured AQC as trained on five qubits remains working efficiently on nine qubits with a minimal amount of additional training cost.
Free Commutative Monoids in Homotopy Type Theory

We develop a constructive theory of finite multisets in Homotopy Type Theory, defining them as free commutative monoids. After recalling basic structural properties of the free commutative-monoid construction, we formalise and establish the categorical universal property of two, necessarily equivalent, algebraic presentations of free commutative monoids using 1-HITs. These presentations correspond to two different equational theories invariably including commutation axioms. In this setting, we prove important structural combinatorial properties of finite multisets. These properties are established in full generality without assuming decidable equality on the carrier set. As an application, we present a constructive formalisation of the relational model of classical linear logic and its differential structure. This leads to constructively establishing that free commutative monoids are conical refinement monoids. Thereon we obtain a characterisation of the equality type of finite multisets and a new presentation of the free commutative-monoid construction as a set-quotient of the list construction. These developments crucially rely on the commutation relation of creation/annihilation operators associated with the free commutative-monoid construction seen as a combinatorial Fock space.
GFST: Gender-Filtered Self-Training for More Accurate Gender in Translation

Targeted evaluations have found that machine translation systems often output incorrect gender in translations, even when the gender is clear from context.Furthermore, these incorrectly gendered translations have the potential to reflect or amplify social biases.We propose gender-filtered self-training (GFST) to improve gender translation accuracy on unambiguously gendered inputs.Our GFST approach uses a source monolingual corpus and an initial model to generate gender-specific pseudo-parallel corpora which are then filtered and added to the training data.We evaluate GFST on translation from English into five languages, finding that it improves gender accuracy without damaging generic quality.We also show the viability of GFST on several experimental settings, including re-training from scratch, fine-tuning, controlling the gender balance of the data, forward translation, and back-translation.
Behavior Constraining in Weight Space for Offline Reinforcement Learning

In offline reinforcement learning, a policy needs to be learned from a single pre-collected dataset.Typically, policies are thus regularized during training to behave similarly to the data generating policy, by adding a penalty based on a divergence between action distributions of generating and trained policy.We propose a new algorithm, which constrains the policy directly in its weight space instead, and demonstrate its effectiveness in experiments.
Compressed domain image retargeting in hierarchical wavelet trees

This paper presents an efficient algorithm with content aware spatial scalability for effective displaying on various screens of different sizes. It combines the set partitioning in hierarchical trees (SPIHT) algorithm and the multiscale seam carving (MSC) algorithm in the compressed domain. As seams of insignificant wavelet trees can be discarded as early as possible, the burden of unnecessary computation and memory space is alleviated. Experimental results show that the proposed algorithm is preferable to other existing pixel-domain algorithms.
Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference

Performing event and entity coreference resolution across documents vastly increases the number of candidate mentions, making it intractable to do the full $n^2$ pairwise comparisons. Existing approaches simplify by considering coreference only within document clusters, but this fails to handle inter-cluster coreference, common in many applications. As a result cross-document coreference algorithms are rarely applied to downstream tasks. We draw on an insight from discourse coherence theory: potential coreferences are constrained by the reader's discourse focus. We model the entities/events in a reader's focus as a neighborhood within a learned latent embedding space which minimizes the distance between mentions and the centroids of their gold coreference clusters. We then use these neighborhoods to sample only hard negatives to train a fine-grained classifier on mention pairs and their local discourse features. Our approach achieves state-of-the-art results for both events and entities on the ECB+, Gun Violence, Football Coreference, and Cross-Domain Cross-Document Coreference corpora. Furthermore, training on multiple corpora improves average performance across all datasets by 17.2 F1 points, leading to a robust coreference resolution model for use in downstream tasks where link distribution is unknown.
Relevance in Belief Update

It has been pointed out by Katsuno and Mendelzon that the so-called AGM revision operators, defined by Alchourrón, Gärdenfors and Makinson, do not behave well in dynamically-changing applications. On that premise, Katsuno and Mendelzon formally characterized a different type of belief-change operators, typically referred to as KM update operators, which, to this date, constitute a benchmark in belief update. In this article, we show that there exist KM update operators that yield the same counter-intuitive results as any AGM revision operator. Against this non-satisfactory background, we prove that a translation of Parikh’s relevance-sensitive axiom (P), in the realm of belief update, suffices to block this liberal behaviour of KM update operators. It is shown, both axiomatically and semantically, that axiom (P) for belief update, essentially, encodes a type of relevance that acts at the possible-worlds level, in the context of which each possible world is locally modified, in the light of new information. Interestingly, relevance at the possible-worlds level is shown to be equivalent to a form of relevance that acts at the sentential level, by considering the building blocks of relevance to be the sentences of the language. Furthermore, we concretely demonstrate that Parikh’s notion of relevance in belief update can be regarded as (at least a partial) solution to the frame, ramification and qualification problems, encountered in dynamically-changing worlds. Last but not least, a whole new class of well-behaved, relevance-sensitive KM update operators is introduced, which generalize Forbus’ update operator and are perfectly-suited for real-world implementations.
The Opinion of Slovene (Mother Tongue) Teachers on Distance Learning in Primary Schools

The Slovene language has several roles in the educational process in the Republic of Slovenia, including its role as a subject in the curriculum in its own right. It is a basic general education subject in public primary schools and has the most hours of all of the subjects. All teachers were forced to teach remotely for the first time in the history of education (first during the 2019/20 school year and then in the 2020/21 school year) during the Covid-19 coronavirus pandemic. The results of a survey comprising 348 teachers with the ability to teach the mother tongue at primary school level (grades 1–9 of primary school; 59% were class curriculum teachers and 41% were Slovene language teachers) show, among other things, that teachers mostly have a good attitude towards distance teaching and feel empowered for this type of teaching, although they feel that this method makes them mentally and physically more tired than teaching in the classroom. Among the advantages of distance teaching, teachers mention the greater use of modern information and communication technology, more use of e-material and the opportunity for formal monitoring of students. In their opinion, the biggest problems of distance teaching (of the Slovene language) include: lack of student participation; lack of non-verbal communication, thus creating difficulties in understanding; and technical issues. Most teachers believe that students acquire less knowledge or far less knowledge by distance education than they would from education in the classroom. Teachers who feel more empowered to teach remotely also have a better attitude towards teaching their mother tongue and are more satisfied with the communication aspect with students in distant teaching. Teachers who have received the necessary training for distance teaching as part of their work feel more empowered to teach this way than teachers who have not had such training.
Q-Art Code: Generating Scanning-robust Art-style QR Codes by Deformable Convolution

Quick Response (QR) code is a popular form of matrix barcodes that are widely used to tag online links on print media (e.g., posters, leaflets, and books). However, standard QR codes typically appear as noise-like black/white squares (named modules) which seriously disrupt the attractiveness of their carriers. In this paper, we propose StyleCode-Net, a method to generate novel art-style QR codes which can better match the entire style of their carriers to improve the visual quality. For endowing QR codes with artistic elements, a big challenge is that the scanning-robustness must be preserved after transforming colors and textures. To address these issues, we propose a module-based deformable convolutional mechanism (MDCM) and a dynamic target mechanism (DTM) in StyleCode-Net. MDCM can extract the features of black and white modules of QR codes respectively. Then, the extracted features are fed to DTM to balance the scanning-robustness and the style representation. Extensive subjective and objective experiments show that our art-style QR codes have reached the state-of-the-art level in both visual quality and scanning-robustness, and these codes have the potential to replace standard QR codes in real-world applications.
Digital transformation of Higher Education: what’s next?

The Covid-19 pandemic has exponentially accelerated digital transformation within the higher education sector, and has given us the opportunity to re-think and re-shape how we do things. This article explores what true digital transformation looks like, where it could potentially lead us and what universities of the future might look like.
A Cognitively-Inspired Model for Making Sense of Hasse Diagrams

In this paper, we present a model of the sense-making process for diagrams, and describe it for the case of Hasse diagrams. Sense-making is modeled as the construction of networks of conceptual blends among image schemas and the diagram’s geometric configuration. As a case study, we specify four image schemas and the geometric configuration of a Hasse diagram, with typed FOL theories. In addition, for the diagram geometry, we utilise Qualitative Spatial Reasoning formalisms. Using an algebraic specification language, we can compute conceptual blends as category-theoretic colimits. Our model approaches sense-making as a process where the image schemas and the diagram geometry both structure each other through a complex network of conceptual blends. This yields a final blend in which the sort of inferences we confer to diagrammatic representations emerge. We argue that this approach to sense-making in diagrams is more cognitively apt than the mainstream view of a diagram being a syntactic representation of some underlying logical semantics. Moreover, our model could be applied to various types of stimuli and is thus valuable for the general field of AI.
Conference Report on 2021 IEEE Congress on Evolutionary Computation (IEEE CEC 2021) [Conference Reports]

Presents information on the 2021 IEEE Congress on Evolutionary Computation (IEEE CEC 2021).
Path Follower &amp; Obstacle Avoidance Smart Robot Vehicle: Applications of Arduino Programming

This paper is intended on a practical approach to design a prototype Robotic Vehicle by using application of Arduino Programming.The robotic vehicle follows a specified path using an IR sensor.The designed robotic vehicle was smart enough to follow the path and directions in given space.It moves& navigates automatically on specified black lines marked on surface.A customized programme coding was also done to perform customized function.Such programmed robot may be useful in several mechanical, industrial, medical & Army operations and can save cost of human labor, human life at hazardous location & time.
COMPUTERIZED QUALITATIVE TEXT ANALYSIS

Relevance. In applied psychology, researchers are increasingly faced with the task of analyzing large amounts of text, both verbal and visual. For these purposes, various methods of analysis have been developed and various computer programs have been created. Objective. The possibilities of computerized qualitative text analysis are analyzed. The spectrum of problems solved by various methods of qualitative text analysis is considered. Methods. The theoretical analysis of classical methods of qualitative text analysis is carried out. The specificity of the method of computerized discourse analysis is analyzed. The Internet searched for the most popular programs for qualitative text analysis, considered the main options and requirements for working in the program. Results. Methods of content-, discourse-, intent-analysis, conversion and narrative analysis are briefly characterized. The computerized discourse analysis (CMDA) approach is described. An approach to solving problems of various methods of qualitative analysis of text in computer analysis of qualitative data (Computer Assisted Qualitative Data Analysis; CAQDAS) is presented. The possibilities of some software packages developed within the CAQDAS approach are considered. Conclusions. The advantages of using computerized discourse analysis are shown. The topic of research of methodological problems accompanying the use of CAQDAS is outlined.
Text Summarization Technique for Punjabi Language Using Neural Networks

In the contemporary world, utilization of digital content has risen exponentially. For example, newspaper and web articles, status updates, advertisements etc. have become an integral part of our daily routine. Thus, there is a need to build an automated system to summarize such large documents of text in order to save time and effort. Although, there are summarizers for languages such as English since the work has started in the 1950s and at present has led it up to a matured stage but there are several languages that still need special attention such as Punjabi language. The Punjabi language is highly rich in morphological structure as compared to English and other foreign languages. In this work, we provide three phase extractive summarization methodology using neural networks. It induces compendious summary of Punjabi single text document. The methodology incorporates pre-processing phase that cleans the text; processing phase that extracts statistical and linguistic features; and classification phase. The classification based neural network applies an activation function- sigmoid and weighted error reduction-gradient descent optimization to generate the resultant output summary. The proposed summarization system is applied over monolingual Punjabi text corpus from Indian languages corpora initiative phase-II. The precision, recall and F-measure are achieved as 90.0%, 89.28% an 89.65% respectively which is reasonably good in comparison to the performance of other existing Indian languages’ summarizers.
Facing the Pandemic

This chapter assesses the legal framework put in place at the national, the subnational (regional), and the municipal levels to face the Covid-19 emergency. It seeks to identify how different measures and actors in the management of the pandemic relate to each other and points out inconsistencies and synergies as well as their impact on Italy's highly asymmetric regionalism. The different responses by the territories revealed both the potential of the country's asymmetric territorial governance and the weaknesses of its incomplete, quasi-federal system: the unclear division of powers between the centre and the regions; weak intergovernmental relations; and the high degree of differentiation in powers, administrative capacity, and political strength among the regions.
Formalization of the prediction and ranking of software development life cycle models

The study of software engineering professional practices includes the use of the formal methodology in a software development. Identifying the appropriate methodology will not only reduce the failure of software but will also help to deliver the software in accordance with the predetermined budget and schedule. In literature, few works have been developed a tool for prediction of the most appropriate methodology for the specific software project. In this paper, a method for selecting an appropriate software development life cycle (SDLC) model based on a ranking manner from the highest to the lowest scoring is presented. The selection and ranking of appropriate SDLC elaborate the related SDLC’s critical factors, these factors are given different weights according to the SDLC, then these weights are used by the proposed mathematical method. The proposed approach has been extensively experimented on a dataset by software practitioners who are working in the software industry. Experimental results show that, the proposed method represents an applicable tool in predicting and ranking suitable SDLC models on various types of projects, such as: life-critical systems, commercial uses systems, and entertainment applications.
Intensify Cloud Security and Privacy Against Phishing Attacks

The world of computation has shifted from centralized (client-server, not web-based) to distributed systems during the last three decades. We are now reverting to virtual centralization, i.e., Cloud Computing (CC). In the world of computation, the location of data and processes makes all the difference. A person has complete control over the data and operations in their computer. On the other side, CC involves a vendor providing service and data upkeep. At the same time, the client/customer is ignorant of where the processes are operating or where the data is kept. As a result, the client does not influence it and doesn't have the right to do it. When it comes to data security in cloud computing, the vendor must guarantee service level agreements (SLAs) to persuade the client. As a result, the SLA must define several degrees of security and their complexity depending on the benefits for the client to comprehend the security rules in place. Phishing is a social engineering assault that is frequently used to obtain user information, such as login passwords and credit card details. It happens when an attacker poses as a trustworthy entity and tricks the victim into opening an e-mail, instant message, or text message. In this research paper, the methodology that tries to identify the phishing attack in the cloud eco-system has been explored and mentioned. The approach used here classifies the malicious and non-malicious URLs.
Toxicity Detection in Online Comments with Limited Data: A Comparative Analysis

We present a comparative study on toxicity detection, focusing on the problem of identifying toxicity types of low prevalence and possibly even unobserved at training time.For this purpose, we train our models on a dataset that contains only a weak type of toxicity, and test whether they are able to generalize to more severe toxicity types.We find that representation learning and ensembling exceed the classification performance of simple classifiers on toxicity detection, while also providing significantly better generalization and robustness.All models benefit from a larger training set size, which even extends to the toxicity types unseen during training.
Design and Implementation of Continuous Authentication Mechanism Based on Multimodal Fusion Mechanism

Most of the current authentication mechanisms adopt the “one-time authentication,” which authenticate users for initial access. Once users have been authenticated, they can access network services without further verifications. In this case, after an illegal user completes authentication through identity forgery or a malicious user completes authentication by hijacking a legitimate user, his or her behaviour will become uncontrollable and may result in unknown risks to the network. These kinds of insider attacks have been increasingly threatening lots of organizations, and have boosted the emergence of zero trust architecture. In this paper, we propose a Multimodal Fusion-based Continuous Authentication (MFCA) scheme, which collects multidimensional behaviour characteristics during the online process, verifies their identities continuously, and locks out the users once abnormal behaviours are detected to protect data privacy and prevent the risk of potential attack. More specifically, MFCA integrates the behaviours of keystroke, mouse movement, and application usage and presents a multimodal fusion mechanism and trust model to effectively figure out user behaviours. To evaluate the performance of the MFCA, we designed and implemented the MFCA system and the experimental results show that the MFCA can detect illegal users in quick time with high accuracy.
On Semantic Cognition, Inductive Generalization, and Language Models

My doctoral research focuses on understanding semantic knowledge in neural network models trained solely to predict natural language (referred to as language models, or LMs), by drawing on insights from the study of concepts and categories grounded in cognitive science. I propose a framework inspired by 'inductive reasoning,' a phenomenon that sheds light on how humans utilize background knowledge to make inductive leaps and generalize from new pieces of information about concepts and their properties. Drawing from experiments that study inductive reasoning, I propose to analyze semantic inductive generalization in LMs using phenomena observed in human-induction literature, investigate inductive behavior on tasks such as implicit reasoning and emergent feature recognition, and analyze and relate induction dynamics to the learned conceptual representation space.