Reviewerly/exHarmony huggingface dataset:
Enhancing Semantic Understanding with Self-Supervised Methods for Abstractive Dialogue Summarization

Contextualized word embeddings can lead to state-of-the-art performances in natural language understanding. Recently, a pre-trained deep contextualized text encoder such as BERT has shown its potential in improving natural language tasks including abstractive summarization. Existing approaches in dialogue summarization focus on incorporating a large language model into summarization task trained on large-scale corpora consisting of news articles rather than dialogues of multiple speakers. In this paper, we introduce self-supervised methods to compensate shortcomings to train a dialogue summarization model. Our principle is to detect incoherent information flows using pretext dialogue text to enhance BERT's ability to contextualize the dialogue text representations. We build and fine-tune an abstractive dialogue summarization model on a shared encoder-decoder architecture using the enhanced BERT. We empirically evaluate our abstractive dialogue summarizer with the SAMSum corpus, a recently introduced dataset with abstractive dialogue summaries. All of our methods have contributed improvements to abstractive summary measured in ROUGE scores. Through an extensive ablation study, we also present a sensitivity analysis to critical model hyperparameters, probabilities of switching utterances and masking interlocutors.
ResearchGuides: Instructor Information: Library Services and Syllabus Info

Information on scheduling orientations and integrating library resources into Canvas. More to come!
Safe-Error Analysis of Post-Quantum Cryptography Mechanisms

The NIST selection process for standardizing Post-Quantum Cryptography Mechanisms is currently running. Many papers already studied their theoretical security, but the resistance in deployed device has not been much investigated so far. In particular, fault attack is a serious threat for algorithms implemented in embedded devices. One particularly powerful technique is to used safe-error attacks. Such attacks exploit the fact that a specific fault may or may not lead to a faulty output depending on a secret value. In this paper, we investigate the resistance of various Post-Quantum candidates algorithms against such attacks.
Integrated Security System Implementation for Network Intrusion

Network security systems vary much according to the circumstances and conditions concerned. A network security system plays a very important role in maintaining network security to prevent attacks and protect us from frequent attacks on a device through a network both in terms of malware administration and data theft. This research aims to build a Honeypot security system as a trap, detect attacks, and be able to get useful information from malware analysis results. It is also focused on the extent to which HIDS-based IDS can detect attacks common in the network, with Honeypot Dionaea, which serves as an attracter for attackers, and what information will be obtained when performing analysis malware using Cuckoo Sandbox. This implementation is carried out with six active users in one network and pays attention to whether IDS can detect the attacker. The results show that HIDS-based IDS has the advantage of monitoring digital data, and based on the results of brute force attack attempts obtained, 65.55% detected an attempt to log in using an unregistered username, 29.16% detected a failed login attempt, 4.17% detected double log in a short time, and 1.11% detected a brute force attempt to gain access to the system. Cuckoo Sandbox can provide malware information in the form of what types of malware are analyzed, how the malware behaves, and how it impacts the malware on the systems attacked. Keywords: Honeypot, Intrusion Detection System, malware, network, security.
Fourth International Workshop on Languages for Modelling Variability (MODEVAR@SPLC 2021)

Feature models were invented in 1990 and have been recognised as one of the main contributions to the Software Product Line community. Although there have been several attempts to establish and study a sort of standard variability modelling language, there is still no consensus on a simple feature modelling language. There can be many motivations to have one but among others, there is one that is very important: information sharing among researchers, tools or developers. Following the spirit of the first three editions, this workshop is an interactive event where all participants shall share knowledge, but also ongoing realizations about how to build up a simple feature modelling language that all the community can agree on.
Implementasi Algoritma Genetika pada Perancangan Aplikasi Penjadwalan Instalasi Antivirus Berbasis Website menggunakan Metode Waterfall

Scheduling is the activity of planning in conducting activity at a certain time. The main objective of the scheduling is to improve the effectiveness and the efficiency of the use of resources so that progress that has been determined previously achieved well. A genetic algorithm is one of the most often algorithms used in optimization scheduling. This algorithm works based on a natural process carried out using heuristic methods. One of the scheduling problems at company x in the step to increase security through system security updates with this antivirus installation program involves all employees as users of each division and handled by the IT team as the pic that handles the program. Scheduling is still compiled manually so that raises some of the problems starting from the preparation process to the division of the schedules to require a long time, it usually tends to clashes between the hour and the day when the PIC handles the user due to not automatic scheduling. Therefore, designing an application system by using web-based programming is conducted waterfall method to ease in the process of the preparation and distribution of a schedule which is more systematic with the implementation of the method of the genetic algorithm so that the scheduling that there will be more optimal.
Deriving Explanation of Deep Visual Saliency Models.

Deep neural networks have shown their profound impact on achieving human level performance in visual saliency prediction. However, it is still unclear how they learn the task and what it means in terms of understanding human visual system. In this work, we develop a technique to derive explainable saliency models from their corresponding deep neural architecture based saliency models by applying human perception theories and the conventional concepts of saliency. This technique helps us understand the learning pattern of the deep network at its intermediate layers through their activation maps. Initially, we consider two state-of-the-art deep saliency models, namely UNISAL and MSI-Net for our interpretation. We use a set of biologically plausible log-gabor filters for identifying and reconstructing the activation maps of them using our explainable saliency model. The final saliency map is generated using these reconstructed activation maps. We also build our own deep saliency model named cross-concatenated multi-scale residual block based network (CMRNet) for saliency prediction. Then, we evaluate and compare the performance of the explainable models derived from UNISAL, MSI-Net and CMRNet on three benchmark datasets with other state-of-the-art methods. Hence, we propose that this approach of explainability can be applied to any deep visual saliency model for interpretation which makes it a generic one.
The IDLAB VoxCeleb Speaker Recognition Challenge 2020 System Description

In this technical report we describe the IDLAB top-scoring submissions for the VoxCeleb Speaker Recognition Challenge 2020 (VoxSRC-20) in the supervised and unsupervised speaker verification tracks. For the supervised verification tracks we trained 6 state-of-the-art ECAPA-TDNN systems and 4 Resnet34 based systems with architectural variations. On all models we apply a large margin fine-tuning strategy, which enables the training procedure to use higher margin penalties by using longer training utterances. In addition, we use quality-aware score calibration which introduces quality metrics in the calibration system to generate more consistent scores across varying levels of utterance conditions. A fusion of all systems with both enhancements applied led to the first place on the open and closed supervised verification tracks. The unsupervised system is trained through contrastive learning. Subsequent pseudo-label generation by iterative clustering of the training embeddings allows the use of supervised techniques. This procedure led to the winning submission on the unsupervised track, and its performance is closing in on supervised training.
Students’ Literacy Abilities in Reading Comprehension through Cooperative Integrated Reading Composition (CIRC) Model

In general, students’ literacy in Sumedang district was categorized as low. Therefore, this qualitative study, conducted to 25 students at Panyingkiran Elementary School 3 Sumedang grade 4th, was carried out to investigate students’ literacy in reading comprehension by applying Cooperative Integrated Reading and Composition (CIRC). The data was collected through observation and questionnaire then strengthening with interview data. Data obtained from these instruments was analyzed qualitatively. The findings indicate that: (1) the use of Cooperative Integrated Reading and Composition (CIRC) learning model could enhance students’ literacy; (2) the use of Cooperative Integrated Reading and Composition (CIRC) could develop students’ initial literacy, in poor, mediocre, or high level; (3) the use of Cooperative Integrated Reading and Composition (CIRC) could develop students’ initial literacy, from poor to mediocre; and (4) the level of literary comprehension was at inferential level
The Effect of “Good First Issue” Indicators upon Newcomer Developers: Identifying Improvements for Newcomer Task Recommendation

The recommendation of tasks for newcomers within a software project throughgood first issues is being done within the domain of software development, such as onGithubplatform. These issues aim to help newcomers identify tasks that are suitablefor them and their level of expertise within the project. This thesis report investigatesthe effectiveness regarding developer onboarding and task completion ofgood firstissues by data mining a set of 105 repositories and manually analyzing at most 30good first issues and 30 initial commits per sampled project. It was found that, althoughgood first issues are effective at developer onboarding, and developers perceivegoodfirst issues as being useful, changes can be made to the types of tasks suggested asgood first issues to match the types of initial contributions made by newcomers. It wasalso found that developers with less than a year of experience favoreddocumentation-related contributions for their first commit to a project.
DEVELOPMENT OF A SYSTEM FOR DIGITAL WATERMARKS EMBEDDING INTO IMAGES BASED ON DCT-LWT-SVD

Copyright protection of digital content is a rather actual problem of humanity in the 21st century. Misuses of multimedia content is very common, and their number is growing with each passing day. One type of copyright protection is the embedding of digital watermark (DW) in the content. In this paper a new method of embedding digital watermark into image using discrete cosine transform, lifting wavelet transform (LWT) with maternal wavelet "Dobeshi-8" and singular coefficients decomposition is proposed. Embedding is performed into the first singular number of the low frequency wavelet transform region. As a digital watermark, we will use a grayscale image normalized to a range from zero to ten to provide a high peak signal-to-noise ratio (PSNR). The research analyzed the developed method: the method of embedding and detecting information was tested for its resistance to various types of attacks, namely: application of noise overlay (Gauss and pulse noise, "salt and pepper"), "unsharp" filter and median filter, and compression attack (with quality coefficients for a complete container from 60 to 100). As a result of the conducted testing, it was established that the method is quite resistant to all the attacks, except for the "unsharp" filtering (the resulting performance is not satisfactory). The method showed good results in peak signal-to-noise ratio - the average PSNR value is 50.5 dB, as well as high rates of similarity between the embedded DW and the extracted one - from 77% to 97.6% while saving the full container in a lossless format, and up to 53, 05 dB and 91.96% while saving the image in a lossless format (JPEG).
Multi-Agent Terraforming: Efficient Multi-Agent Path Finding via Environment Manipulation

Planning collision-free paths for multiple agents operating in close proximity has a myriad of applications ranging from smart warehouses to route planning for airport taxiways. This problem, known as the Multi-Agent Path-Finding (MAPF) problem, is highly relevant to real-world applications in automation and robotics, and has attracted significant research in recent years. While in many applications, the robots are tasked with transporting objects and thus have the means to move obstacles, common formulations of the problem prohibit agents from moving obstacles en-route to a task. This often causes agents to take long detours to avoid obstacles instead of simply moving them to clear a path. In this work we present multi-agent terraforming, a novel extension of the MAPF problem that can exploit the fact that the system contains movable obstacles. We build upon leading MAPF solvers and propose an efficient method to solve the multi-agent terraforming problem in a manner that is both complete and optimal. We evaluate our method on scenarios inspired by smart warehouses (such as those of Amazon) and demonstrate that, compared to the classical MAPF formulation, the extra flexibility provided by terraforming facilitates a notable improvement of solution quality.
SOCIAL MEDIA AND THE CHANGING PHASE OF LANGUAGE: RE-DEFINING THE DYNAMICS OF ENGLISH LANGUAGE USAGE IN NIGERIA

The 21 st  Century is a digital age and computer mediated communication has been quite indispensable as it has taken language and communication to another level. Thus, social media platforms have been in the forefront of championing a change in the use of language, thereby creating new varieties of language. This paper investigates the place of social media in changing the phase of English language and re-defining the dynamics of usage of the language. Espousing William Labov’s Social Network Analysis theory, the data for the study was obtained from four social media platforms-facebook, twitter, whatsapp and instagram-and a total of eighty samples which included words, phrases and sentences were randomly selected and analysed. The findings reveal that social media language is characterized by the use of alphanumeric words such as Fei8, 2wef, 9lon etc; acronyms and alphabetisms such as TFY, YOLO, POTD etc; neologisms such as oyocracy, boo, kinging; blends such as sexting, provex, fantbulous; abnormal spellings etc that are unique and have created a new variety of English used in Nigeria. The study concludes that social media language is quite unconventional and dynamic.
VaxxStance@IberLEF 2021: Overview of the Task on Going Beyond Text in Cross-Lingual Stance Detection

This work has been partially supported by the European Social Fund through the Youth Employment Initiative (YEI 2019) and the Spanish Ministry of Science, Innovation and Universities (DeepReading RTI2018-096846-B-C21, MCIU/AEI/FEDER, UE), and by the DeepText project (KK-2020/00088), funded by the Basque Government. Rodrigo Agerri is also funded by the RYC-2017-23647 fellowship.
The ByteDance Speaker Diarization System for the VoxCeleb Speaker Recognition Challenge 2021

This paper describes the ByteDance speaker diarization system for the fourth track of the VoxCeleb Speaker Recognition Challenge 2021 (VoxSRC-21). The VoxSRC-21 provides both the dev set and test set of VoxConverse for use in validation and a standalone test set for evaluation. We first collect the duration and signal-to-noise ratio (SNR) of all audio and find that the distribution of the VoxConverse's test set and the VoxSRC-21's test set is more closer. Our system consists of voice active detection (VAD), speaker embedding extraction, spectral clustering followed by a re-clustering step based on agglomerative hierarchical clustering (AHC) and overlapped speech detection and handling. Finally, we integrate systems with different time scales using DOVER-Lap. Our best system achieves 5.15\% of the diarization error rate (DER) on evaluation set, ranking the second at the diarization track of the challenge.
Level Set Binocular Stereo with Occlusions

Localizing stereo boundaries and predicting nearby disparities are difficult because stereo boundaries induce occluded regions where matching cues are absent. Most modern computer vision algorithms treat occlusions secondarily (e.g., via left-right consistency checks after matching) or rely on high-level cues to improve nearby disparities (e.g., via deep networks and large training sets). They ignore the geometry of stereo occlusions, which dictates that the spatial extent of occlusion must equal the amplitude of the disparity jump that causes it. This paper introduces an energy and level-set optimizer that improves boundaries by encoding occlusion geometry. Our model applies to two-layer, figure-ground scenes, and it can be implemented cooperatively using messages that pass predominantly between parents and children in an undecimated hierarchy of multi-scale image patches. In a small collection of figure-ground scenes curated from Middlebury and Falling Things stereo datasets, our model provides more accurate boundaries than previous occlusion-handling stereo techniques. This suggests new directions for creating cooperative stereo systems that incorporate occlusion cues in a human-like manner.
Goal-driven text descriptions for images

A big part of achieving Artificial General Intelligence(AGI) is to build a machine that can see and listen like humans. Much work has focused on designing models for image classification, video classification, object detection, pose estimation, speech recognition, etc., and has achieved significant progress in recent years thanks to deep learning. However, understanding the world is not enough. An AI agent also needs to know how to talk, especially how to communicate with a human. While perception (vision, for example) is more common across animal species, the use of complicated language is unique to humans and is one of the most important aspects of intelligence. In this thesis, we focus on generating textual output given visual input. In Chapter 3, we focus on generating the referring expression, a text description for an object in the image so that a receiver can infer which object is being described. We use a comprehension machine to directly guide the generated referring expressions to be more discriminative. In Chapter 4, we introduce a method that encourages discriminability in image caption generation. We show that more discriminative captioning models generate more descriptive captions. In Chapter 5, we study how training objectives and sampling methods affect the models' ability to generate diverse captions. We find that a popular captioning training strategy will be detrimental to the diversity of generated captions. In Chapter 6, we propose a model that can control the length of generated captions. By changing the desired length, one can influence the style and descriptiveness of the captions. Finally, in Chapter 7, we rank/generate informative image tags according to their information utility. The proposed method better matches what humans think are the most important tags for the images.
Matching Underwater Sonar Images by the Learned Descriptor Based on Style Transfer Method

This paper proposes a method that combines the style transfer technique and the learned descriptor to enhance the matching performances of underwater sonar images. In the field of underwater vision, sonar is currently the most effective long-distance detection sensor, it has excellent performances in map building and target search tasks. However, the traditional image matching algorithms are all developed based on optical images. In order to solve this contradiction, the style transfer method is used to convert the sonar images into optical styles, and at the same time, the learned descriptor with excellent expressiveness for sonar images matching is introduced. Experiments show that this method significantly enhances the matching quality of sonar images. In addition, it also provides new ideas for the preprocessing of underwater sonar images by using the style transfer approach.
Generation, augmentation, and alignment: A pseudo-source domain based method for source-free domain adaptation

Conventional unsupervised domain adaptation (UDA) methods need to access both labeled source samples and unlabeled target samples simultaneously to train the model. While in some scenarios, the source samples are not available for the target domain due to data privacy and safety. To overcome this challenge, recently, source-free domain adaptation (SFDA) has attracted the attention of researchers, where both a trained source model and unlabeled target samples are given. Existing SFDA methods either adopt a pseudo-label based strategy or generate more samples. However, these methods do not explicitly reduce the distribution shift across domains, which is the key to a good adaptation. Although there are no source samples available, fortunately, we find that some target samples are very similar to the source domain and can be used to approximate the source domain. This approximated domain is denoted as the pseudo-source domain. In this paper, inspired by this observation, we propose a novel method based on the pseudo-source domain. The proposed method firstly generates and augments the pseudo-source domain, and then employs distribution alignment with four novel losses based on pseudo-label based strategy. Among them, a domain adversarial loss is introduced between the pseudo-source domain the remaining target domain to reduce the distribution shift. The results on three real-world datasets verify the effectiveness of the proposed method.