Reviewerly/exHarmony huggingface dataset:
Multi-task Learning for Paraphrase Generation With Keyword and Part-of-Speech Reconstruction

Paraphrase generation using deep learning has been a research hotspot of natural language processing in the past few years.While previous studies tackle the problem from different aspects, the essence of paraphrase generation is to retain the key semantics of the source sentence and rewrite the rest of the content.Inspired by this observation, we propose a novel two-stage model, PGKPR, for paraphrase generation with keyword and partof-speech reconstruction.The rationale is to capture simultaneously the possible keywords of a source sentence and the relations between them to facilitate the rewriting.In the first stage, we identify the possible keywords using a prediction attribution technique, where the words obtaining higher attribution scores are more likely to be the keywords.In the second stage, we train a transformer-based model via multitask learning for paraphrase generation.The novel learning task is the reconstruction of the keywords and part-of-speech tags, respectively, from a perturbed sequence of the source sentence.The learned encodings are then decoded to generate the paraphrase.We conduct the experiments on two commonly-used datasets, and demonstrate the superior performance of PGKPR over comparative models on multiple evaluation metrics.
Normalized Attraction Travel Personality Representation for Improving Travel Recommender Systems

Travel recommender systems (TRSs) aim to reduce travel-related search overload.A significant part of a TRS is representing attractions in a way that reflect the explicit and implicit features of attractions.However, traditional attraction representation methods may not provide a complete image of attractions.Building on the notions of user travel styles (UTSs) and the wisdom of crowds, we propose a method derived from topic-model-based models to represent travel attractions, called the Normalized Attraction Travel Personality (NATP) representation.This approach attempts to leverage the semantics of attraction reviews to model user travel personalities (UTPs), which collectively can construct the attraction travel personality (ATP) representation.Furthermore, we regularize and normalize the ATP representation to obtain our proposed representation.This NATP-based attraction representation could capture implicit characteristics of attractions revealed by the wisdom of crowds.Our experiments show that our representation method gained better results when evaluated against comparative approaches in terms of rating prediction and recommendation ranking quality, indicating the effectiveness of the proposed attraction representation.Lastly, we qualitatively investigate how our attraction representation surpasses the state-ofthe-art representation methods.
Explaining a Deep Neural Model with Hierarchical Attention for Aspect-Based Sentiment Classification Using Diagnostic Classifiers

LCR-Rot-hop++ is a state-of-art model for Aspect-Based Sentiment Classification. However, it is also a black-box model where the information encoded in each layer is not understood by the user. This study uses diagnostic classifiers, single layer neural networks, to evaluate the information encoded in each layer of the LCR-Rot-hop++ model. This is done by using various hypotheses designed to test for information deemed useful for sentiment analysis. We conclude that the model did not focus on identifying the aspect mentions associated with a word and the structure of the sentence. However, the model excelled in encoding information to identify which words are related to the target. Lastly, the model was able to encode to some extent information about the word sentiment and sentiments of the words related to the target.
A Heuristic Algorithm for Minimizing Server Maintenance Time and Vulnerability Surface on Data Centers

As cyberattacks against the cloud become more frequent, operators must define efficient maintenance strategies to safeguard data centers. Existing maintenance strategies strive to minimize the maintenance duration and the number of migrations. However, such solutions overlook the period that servers wait for their update, which represents a vulnerability window that attackers can exploit. Accordingly, this study introduces a novel metric, Vulnerability Surface, which assesses maintenance strategies regarding servers' exposure. In addition, we present Salus, a heuristic that minimizes servers' exposure during maintenance. Experimental results show that Salus reduces the Vulnerability Surface by 19.44% compared to baseline strategies.
Intelligibility Improvement of Esophageal Speech Using Sequence-to-Sequence Voice Conversion with Auditory Attention

Laryngectomees are individuals whose larynx has been surgically removed, usually due to laryngeal cancer. The immediate consequence of this operation is that these individuals (laryngectomees) are unable to speak. Esophageal speech (ES) remains the preferred alternative speaking method for laryngectomees. However, compared to the laryngeal voice, ES is characterized by low intelligibility and poor quality due to chaotic fundamental frequency F0, specific noises, and low intensity. Our proposal to solve these problems is to take advantage of voice conversion as an effective way to improve speech quality and intelligibility. To this end, we propose in this work a novel esophageal–laryngeal voice conversion (VC) system based on a sequence-to-sequence (Seq2Seq) model combined with an auditory attention mechanism. The originality of the proposed framework is that it adopts an auditory attention technique in our model, which leads to more efficient and adaptive feature mapping. In addition, our VC system does not require the classical DTW alignment process during the learning phase, which avoids erroneous mappings and significantly reduces the computational time. Moreover, to preserve the identity of the target speaker, the excitation and phase coefficients are estimated by querying a binary search tree. In experiments, objective and subjective tests confirmed that the proposed approach performs better even in some difficult cases in terms of speech quality and intelligibility.
A post quantum secure construction of an authentication protocol for satellite communication

Summary Satellite's communication system is used to communicate under significant distance and circumstances where the other communication systems are not comfortable. Since all the data are exchanged over a public channel, so the security of the data is an essential component for the communicating parties. Both key exchange and authentication are two cryptographic tools to establish a secure communication between two parties. Currently, various kinds of authentication protocols are available to establish a secure network, but all of them depend on number–theoretical (discrete logarithm problem/factorization assumption) hard assumptions. Due to Shor's and Grover's computing algorithm number theoretic assumptions are breakable by quantum computers. Although Kumar and Garg have proposed a quantum attack‐resistant protocol for satellite communication, it cannot resist stolen smart card attack. We have analyzed that how Kumar and Garg is vulnerable to the stolen smart card attack using differential power analysis attack described in He et al and Chen and Chen. We have also analyzed the modified version of signal leakage attack and sometimes called improved signal leakage attack on Kumar and Garg's protocol. We have tried to construct a secure and efficient authentication protocol for satellites communication that is secure against quantum computing. This is more efficient as it requires only three messages of exchange. This paper includes security proof and performance of the proposed authentication and key agreement protocol.
DATS_2022: A Versatile Indian Dataset for Object Detection in Unstructured Traffic Conditions

Driver Assistance System has proved to be the best alternative to the autonomous vehicles in countries like India which has heavy and irregular traffic conditions. It is a challenging task for a machine to visually understand the traffic scenes in a complex environment. To develop a machine learning algorithm for such an environment, there is a need of a dataset that may provide the correct information of the traffic scene. The proposed dataset, DATS_2022, is a comprehensive dataset for object detection specific to Indian traffic scenario. The dataset consists of images captured using a high-resolution camera in an android phone. The images are annotated using a free, open-source tool. XML files for these annotations are generated and saved which are used to extract the labels for training various machine learning algorithms. DATS_2022 is a complete dataset with images from rural as well as urban Indian traffic scenes. The dataset consists of more than 10000 images with 45object classes. There are more than 7000 annotations in different formats along with the dataset till the submission of this work. It aims to support research in the area of object detection and classification using deep and machine learning algorithms.
Impact of ICTs on academic development and the creation of educational public policies in times of pandemic

Information and communication technologies (ICT) have become an important source of innovation and increased efficiency for many sectors around the world. Particularly in the educational sector, the application of ICT has become a fundamental part of the learning process of students at all levels of education, both outside and inside the classroom, as a result of the improvement in learning methods, the teaching, research and development, elements that somehow affect academic development. For this reason, the governments of the various countries have established public policies aimed at improving and developing the educational system through ICT, especially as a result of the crisis that the pandemic has generated in the development of teaching processes. This study is based on a bibliographic review with the aim of analyzing the influence of ICT on student academic development, in addition to presenting qualitative research, some statistical data on the impact of virtual education due to confinement and how countries have developed public policies based on the use of the various technological tools available in the educational system and everything in the environment of the current global pandemic, information that will allow to form a firm criterion about the future of education focused on ICT.
Transforming Higher Education Using WebVR: A Case Study

The transformation of education through emerging technologies has been an imperative due to the pandemic of COVID-19, which has forced higher education institutions to propose strategies to provide better experiences for their students. The objective of this research was to identify the challenges and opportunities of using WebVR tools for the development of academic activities, with the intention of interpreting how these technological tools are combined with educational practices among teachers and students. The context was placed in an activity of a graduate level course in online format, in a private higher education institution in Mexico. A qualitative study was conducted with a case study design to account for the perception of the participants regarding the value of the user experience, the analysis of the integrated tools and the contribution of this WebVR tool to the development of competencies in educational practice. The results show that if the technical requirements and a basic level of appropriation of digital competencies are met, including this type of emerging technology will bring benefits to the educational practice, such as the development of transversal and disciplinary competencies, improvement of interaction and socialization of participants, as well as motivation by incorporating playful elements.
LA INFLUENCIA CROSS-LINGÜÍSTICA EN LA ATRICIÓN DE LA L1 Y EN LA ADQUISICIÓN SIMULTÁNEA: EVIDENCIA DE BILINGÜES ITALIANOS / TURCOS

The present study reports data from a 40-item acceptability judgment task in Italian on the interpretation of backward anaphora in complex sentences in which three groups participated.The groups included bilingual Italian native speakers highly proficient in Turkish as a second language (L2), and Turkish and Italian simultaneous bilingual (2L1) children and monolingual native Italian speakers as a control group.In the 40-item acceptability judgment task in Italian, they were asked the degree of acceptability of sentences introduced by short stories suggesting coreference or disjoint reading of the overt or null pronoun.It was assumed that Italian and Turkish languages do not differ with respect to the antecedent biases of null and overt subject pronouns in the contexts under investigation, except for anaphoric pronoun "kendi" that when it is used as the thirdperson singular or plural, always expressing anaphoric references with the subject in the matrix sentence.The findings revealed a significant difference in the monolingual group regarding the null pronoun when preceded by a quantifier.This is discussed as evidence for the cross-linguistic influence at the syntax-discourse interface in 2L1 acquisition in children and in L1 attrition in language with similar parametric settings and for the fact that quality and quantity of input in the native or minority language can significantly diminish the effect.
Network Traffic Classification using ML: A Comparative Analysis

Traffic Classification methods aim for the automation of processes of analysis and categorization of traffic flowing through a network according to its intrinsic nature and characteristics. In its first iterations, traffic classification took advantage of aspects such as port numbers and payload analysis for categorization, however, due to the fast-growing and changing nature of the Internet, such methods became ineffective. Thus, the necessity for newer and more effective forms of traffic classification persists. Recently, Machine Learning (ML) techniques have proven to be an effective tool for analyzing and categorizing traffic within the context of modern networks, however, a number of issues can still emerge when evaluating the effectiveness of such methods in a real-world context. Issues such as overall classification throughput of ML algorithms, classification accuracy and precision, the integrity and/or representativeness of the datasets used in the training phase are important aspects of accessing the effectiveness of ML as a traffic classification method. The present paper analyses and compares a comprehensive set of studies and surveys covering the subject of ML as a tool for traffic classification. Our approach compares the data characteristics used for evaluating the effectiveness of ML algorithms, namely traffic classification in the context of a live network, and discusses issues concerning the creation and usage of datasets to train the ML algorithms so that their future application in real-time traffic can be similar to the results obtained during offline testing.
Participatory AR - A Parametric Design Instrument

CAAD research has frequently investigated the realm of public participation in large scale urban design re-development.Yet, the recurring problem lies with the lay-person often not being able to read 2d and 3d graphic information effectively, and hence be able to participate in the process of design development proactively.To date, much-existing research focuses on developing designs for urban settings using contemporary interaction devices such as the /Hololens/; such devices, with custom interfaces, require a significant level of expertise, or an experienced 'guide', to help navigate or create within these environments.Our paper presents a novel alternative based on real-time-virtual-engines, XR, and a parametric back-end system.The paper discusses the advantages that the resulting tangible user interface (TUI) can play in the lay-person's engagement in the design process.In the paper, we describe how the integration of interaction design (IxD) and augmented reality (AR) offer new opportunities due to the increasing availability of barrier-free technologies that can better include lay-persons as active participants in the design development process.
Reappraising Configuration and its Potential for Collaborative Objects

This year's conference theme `Towards a new configurable architecture`, provides a good starting point for reappraising and reapplying previous concepts of `Configuration' in architectural design.The concept reappears often, but was particularly powerful whenever new computational tools and architectural concepts emerged and revealed strong synergies.In the 1960ties there was such a moment when configuration's pluralistic properties embraced architectural concepts of structuralism and early computing.Therefore this paper looks back at previous concepts of configuration to identify capacities that could inform current synergies of computational tools, such as open platforms, and architectural concepts of the second digital turn in architecture.The way we communicate, access, and exchange information recently accelerated towards realtime sharing of data, bits & pieces, and experiences.Open platforms that enable user-generated content and collective production of value are becoming more common in design.This paper discusses ways in which this collective content production can enable a computational and human-centric architecture, by reappraising previous concepts of configuration such as: open configurations, latent structures and variable infills.
Lightweight Indoor Multi-Object Tracking in Overlapping FOV Multi-Camera Environments

Multi-Target Multi-Camera Tracking (MTMCT), which aims to track multiple targets within a multi-camera network, has recently attracted considerable attention due to its wide range of applications. The main challenge of MTMCT is to match local tracklets (i.e., sub-trajectories) obtained by different cameras and to combine them into global trajectories across the multi-camera network. This paper addresses the cross-camera tracklet matching problem in scenarios with partially overlapping fields of view (FOVs), such as indoor multi-camera environments. We present a new lightweight matching method for the MTMC task that employs similarity analysis for location features. The proposed approach comprises two steps: (i) extracting the motion information of targets based on a ground projection method and (ii) matching the tracklets using similarity analysis based on the Dynamic Time Warping (DTW) algorithm. We use a Kanade-Lucas-Tomasi (KLT) algorithm-based frame-skipping method to reduce the computational overhead in object detection and to produce a smooth estimate of the target's local tracklets. To improve matching accuracy, we also investigate three different location features to determine the most appropriate feature for similarity analysis. The effectiveness of the proposed method has been evaluated through real experiments, demonstrating its ability to accurately match local tracklets.
Pseudo-spherical Knowledge Distillation

Knowledge distillation aims to transfer the information by minimizing the cross-entropy between the probabilistic outputs of the teacher and student network. In this work, we propose an alternative distillation objective by maximizing the scoring rule, which quantitatively measures the agreement of a distribution to the reference distribution. We demonstrate that the proper and homogeneous scoring rule exhibits more preferable properties for distillation than the original cross entropy based approach. To that end, we present an efficient implementation of the distillation objective based on a pseudo-spherical scoring rule, which is a family of proper and homogeneous scoring rules. We refer to it as pseudo-spherical knowledge distillation. Through experiments on various model compression tasks, we validate the effectiveness of our method by showing its superiority over the original knowledge distillation. Moreover, together with structural distillation methods such as contrastive representation distillation, we achieve state of the art results in CIFAR100 benchmarks.
Linear Temporal Logic Modulo Theories over Finite Traces

This paper studies Linear Temporal Logic over Finite Traces (LTLf) where proposition letters are replaced with first-order formulas interpreted over arbitrary theories, in the spirit of Satisfiability Modulo Theories. The resulting logic, called LTLf Modulo Theories (LTLfMT), is semi-decidable. Nevertheless, its high expressiveness comes useful in a number of use cases, such as model-checking of data-aware processes and data-aware planning. Despite the general undecidability of these problems, being able to solve satisfiable instances is a compromise worth studying. After motivating and describing such use cases, we provide a sound and complete semi-decision procedure for LTLfMT based on the SMT encoding of a one-pass tree-shaped tableau system. The algorithm is implemented in the BLACK satisfiability checking tool, and an experimental evaluation shows the feasibility of the approach on novel benchmarks.
RAPQ: Rescuing Accuracy for Power-of-Two Low-bit Post-training Quantization

We introduce a Power-of-Two post-training quantization( PTQ) method for deep neural network that meets hardware requirements and does not call for long-time retraining. PTQ requires a small set of calibration data and is easier for deployment, but results in lower accuracy than Quantization-Aware Training( QAT). Power-of-Two quantization can convert the multiplication introduced by quantization and dequantization to bit-shift that is adopted by many efficient accelerators. However, the Power-of-Two scale has fewer candidate values, which leads to more rounding or clipping errors. We propose a novel Power-of-Two PTQ framework, dubbed RAPQ, which dynamically adjusts the Power-of-Two scales of the whole network instead of statically determining them layer by layer. It can theoretically trade off the rounding error and clipping error of the whole network. Meanwhile, the reconstruction method in RAPQ is based on the BN information of every unit. Extensive experiments on ImageNet prove the excellent performance of our proposed method. Without bells and whistles, RAPQ can reach accuracy of 65% and 48% on ResNet-18 and MobileNetV2 respectively with weight INT2 activation INT4. We are the first to propose PTQ for the more constrained but hardware-friendly Power-of-Two quantization and prove that it can achieve nearly the same accuracy as SOTA PTQ method. The code will be released.
Learning Prototype via Placeholder for Zero-shot Recognition

Zero-shot learning (ZSL) aims to recognize unseen classes by exploiting semantic descriptions shared between seen classes and unseen classes. Current methods show that it is effective to learn visual-semantic alignment by projecting semantic embeddings into the visual space as class prototypes. However, such a projection function is only concerned with seen classes. When applied to unseen classes, the prototypes often perform suboptimally due to domain shift. In this paper, we propose to learn prototypes via placeholders, termed LPL, to eliminate the domain shift between seen and unseen classes. Specifically, we combine seen classes to hallucinate new classes which play as placeholders of the unseen classes in the visual and semantic space. Placed between seen classes, the placeholders encourage prototypes of seen classes to be highly dispersed. And more space is spared for the insertion of well-separated unseen ones. Empirically, well-separated prototypes help counteract visual-semantic misalignment caused by domain shift. Furthermore, we exploit a novel semantic-oriented fine-tuning method to guarantee the semantic reliability of placeholders. Extensive experiments on five benchmark datasets demonstrate the significant performance gain of LPL over the state-of-the-art methods.