Reviewerly/exHarmony huggingface dataset:
GENSF: SIMULTANEOUS ADAPTATION OF GENERATIVE PRE-TRAINED MODELS AND SLOT FILLING

In transfer learning, it is imperative to achieve strong alignment between a pre-trained model and a downstream task. Prior work has done this by proposing task-specific pre-training objectives, which sacrifices the inherent scalability of the transfer learning paradigm. We instead achieve strong alignment by simultaneously modifying both the pre-trained model and the formulation of the downstream task, which is more efficient and preserves the scalability of transfer learning. We present GenSF (Generative Slot Filling), which leverages a generative pre-trained open-domain dialog model for slot filling. GenSF (1) adapts the pre-trained model by incorporating inductive biases about the task and (2) adapts the downstream task by reformulating slot filling to better leverage the pre-trained model's capabilities. GenSF achieves state-of-the-art results on two slot filling datasets with strong gains in few-shot and zero-shot settings. We achieve a 9 F1 score improvement in zero-shot slot filling. This highlights the value of strong alignment between the pre-trained model and the downstream task.
Unsupervised Representation Disentanglement of Text: An Evaluation on Synthetic Datasets

To highlight the challenges of achieving representation disentanglement for text domain in an unsupervised setting, in this paper we select a representative set of successfully applied models from the image domain.We evaluate these models on 6 disentanglement metrics, as well as on downstream classification tasks and homotopy.To facilitate the evaluation, we propose two synthetic datasets with known generative factors.Our experiments highlight the existing gap in the text domain and illustrate that certain elements such as representation sparsity (as an inductive bias), or representation coupling with the decoder could impact disentanglement.To the best of our knowledge, our work is the first attempt on the intersection of unsupervised representation disentanglement and text, and provides the experimental framework and datasets for examining future developments in this direction. 1
Lower and Upper Bounds on the Randomness Complexity of Private Computations of AND

We consider multi-party information-theoretic private protocols, and specifically their randomness complexity. The randomness complexity of private protocols is of interest both because random bits are considered a scarce resource, and because of the relation between that complexity measure and other complexity measures of boolean functions such as the circuit size or the sensitivity of the function being computed [12, 17]. More concretely, we consider the randomness complexity of the basic boolean function and, that serves as a building block in the design of many private protocols. We show that and cannot be privately computed using a single random bit, thus giving the first non-trivial lower bound on the 1-private randomness complexity of an explicit boolean function, $$f: \{0,1\}^n \rightarrow \{0,1\}$$. We further show that the function and, on any number of inputs n (one input bit per player), can be privately computed using 8 random bits (and 7 random bits in the special case of $$n=3$$ players), improving the upper bound of 73 random bits implicit in [17]. Together with our lower bound, we thus approach the exact determination of the randomness complexity of and. To the best of our knowledge, the exact randomness complexity of private computation is not known for any explicit function (except for xor, which is trivially 1-random, and for several degenerate functions).
Complex words identification using word-level features for SemEval-2020 Task 1

This article describes a system to predict the complexity of words for the Lexical Complexity Prediction (LCP) shared task hosted at Se-mEval 2021 (Task 1) with a new annotated English dataset with a Likert scale.Located in the Lexical Semantics track, the task consisted of predicting the complexity value of the words in context.A machine learning approach was carried out based on the frequency of the words and several characteristics added at word level.Over these features, a supervised random forest regression algorithm was trained.Several runs were performed with different values to observe the performance of the algorithm.For the evaluation, our best results reported a M.A.E of 0.07347, M.S.E. of 0.00938, and R.M.S.E. of 0.096871.Our experiments showed that, with a greater number of characteristics, the precision of the classification increases.
Do Bugs Propagate? An Empirical Analysis of Temporal Correlations Among Software Bugs

The occurrences of bugs are not isolated events, rather they may interact, affect each other, and trigger other latent bugs. Identifying and understanding bug correlations could help developers localize bug origins, predict potential bugs, and design better architectures of software artifacts to prevent bug affection. Many studies in the defect prediction and fault localization literature implied the dependence and interactions between multiple bugs, but few of them explicitly investigate the correlations of bugs across time steps and how bugs affect each other. In this paper, we perform social network analysis on the temporal correlations between bugs across time steps on software artifact ties, i.e., software graphs. Adopted from the correlation analysis methodology in social networks, we construct software graphs of three artifact ties such as function calls and type hierarchy and then perform longitudinal logistic regressions of time-lag bug correlations on these graphs. Our experiments on four open-source projects suggest that bugs can propagate as observed on certain artifact tie graphs. Based on our findings, we propose a hybrid artifact tie graph, a synthesis of a few well-known software graphs, that exhibits a higher degree of bug propagation. Our findings shed light on research for better bug prediction and localization models and help developers to perform maintenance actions to prevent consequential bugs.
Modeling of unsupervised knowledge graph of events based on mutual information among neighbor domains and sparse representation

Text event mining, as an indispensable method of text mining processing, has attracted the extensive attention of researchers. A modeling method for knowledge graph of events based on mutual information among neighbor domains and sparse representation is proposed in this paper, i.e. UKGE-MS. Specifically, UKGE-MS can improve the existing text mining technology's ability of understanding and discovering high-dimensional unmarked information, and solves the problems of traditional unsupervised feature selection methods, which only focus on selecting features from a global perspective and ignoring the impact of local connection of samples. Firstly, considering the influence of local information of samples in feature correlation evaluation, a feature clustering algorithm based on average neighborhood mutual information is proposed, and the feature clusters with certain event correlation are obtained; Secondly, an unsupervised feature selection method based on the high-order correlation of multi-dimensional statistical data is designed by combining the dimension reduction advantage of local linear embedding algorithm and the feature selection ability of sparse representation, so as to enhance the generalization ability of the selected feature items. Finally, the events knowledge graph is constructed by means of sparse representation and l1 norm. Extensive experiments are carried out on five real datasets and synthetic datasets, and the UKGE-MS are compared with five corresponding algorithms. The experimental results show that UKGE-MS is better than the traditional method in event clustering and feature selection, and has some advantages over other methods in text event recognition and discovery.
The Multi-Dimensional Actions Control Approach for Obstacle Avoidance Based on Reinforcement Learning

In robotics, obstacle avoidance is an essential ability for distance sensor-based robots. This type of robot has axisymmetrically distributed distance sensors to acquire obstacle distance, so the state is symmetrical. Training the control policy with a reinforcement learning method is a trend. Considering the complexity of environments, such as narrow paths and right-angle turns, robots will have a better ability if the control policy can control the steering direction and speed simultaneously. This paper proposes the multi-dimensional action control (MDAC) approach based on a reinforcement learning technique, which can be used in multiple continuous action space tasks. It adopts a hierarchical structure, which has high and low-level modules. Low-level policies output concrete actions and the high-level policy determines when to invoke low-level modules according to the environment’s features. We design robot navigation experiments with continuous action spaces to test the method’s performance. It is an end-to-end approach and can solve complex obstacle avoidance tasks in navigation.
Smart Contract Design for a Bidding-based Supply Chain System built on an Alliance Blockchain

With the development of marketing, the bidding transactions among enterprises become more and more frequent. The traditional supply chain system cannot meet the needs of enterprise operation, due to the problems like inefficient information sharing, low collaboration, and huge consumption of human resources. To deal with the above problems, the emergence of blockchain technology provides a promising solution. In this paper, we employ an alliance blockchain architecture to realize a bidding-based supply chain system. Specially, we design a smart contract with reasonable contract interaction rules for the whole process a bidding transaction, with two specific functions, i.e., risk early warning and intelligent bid evaluating. In addition, a real alliance blockchain is implemented based on the Hyperledger platform, to validate the feasibility of our designed smart contract.
Symbolic and Sub-Symbolic Systems in People and Machines

Author(s): Valentin, Simon; Zhao, Bonan; Jiang, Chentian; Bramley, Neil R; Lucas, Chris | Abstract: To what extent is symbolic processing required for intelligent behaviour? Advances in both sub-symbolic deep learning systems and explicitly symbolic probabilistic program induction approaches have recently reinvigorated this long standing question about cognition. While sub-symbolic approaches have shown impressive results, they still lag far behind human cognition, e.g., in the compositional re-use of learned concepts or generalizing to new contexts. Symbolic systems have successfully addressed some of these shortcomings, but face other unsolved issues relating to feature selection, thorny search spaces and scalability. This workshop intends to bring together established and newly emerging perspectives on the debate and explore the recently rekindled interest in hybrid architectures.
A Multilayer Security Framework for Cloud Computing in Internet of Things (IoT) Domain

Cloud computing is a type of emerging computing technology that relies on shared computing resources rather than having local servers or personal devices to handle applications.It is an emerging technology that provides services over the internet: Utilizing the online services of different software.Many works have been carried out and various security frameworks relating to the security issues of cloud computing have been proposed in numerous ways.But they do not propose a quantitative approach to analyze and evaluate privacy and security in cloud computing systems.In this research, we try to introduce top security concerns of cloud computing systems, analyze the threats and propose some countermeasures for them.We use a quantitative security risk assessment model to present a multilayer security framework for the solution of the security threats of cloud computing systems.For evaluating the performance of the proposed security framework we have utilized an Own-Cloud platform using a 64-bit quad-core processor based embedded system.Own-Cloud platform is quite literally as any analytics, machine learning algorithms or signal processing techniques can be implemented using the vast variety of Python libraries built for those purposes.In addition, we have proposed two algorithms, which have been deployed in the Own-Cloud for mitigating the attacks and threats to cloud-like reply attacks, DoS/DDoS, back door attacks, Zombie, etc.Moreover, unbalanced RSA based encryption is used to reduce the risk of authentication and authorization.This framework is able to mitigate the targeted attacks satisfactorily.
Comparative Error Analysis in Neural and Finite-state Models for Unsupervised Character-level Transduction

Traditionally, character-level transduction problems have been solved with finite-state models designed to encode structural and linguistic knowledge of the underlying process, whereas recent approaches rely on the power and flexibility of sequence-to-sequence models with attention.Focusing on the less explored unsupervised learning scenario, we compare the two model classes side by side and find that they tend to make different types of errors even when achieving comparable performance.We analyze the distributions of different error classes using two unsupervised tasks as testbeds: converting informally romanized text into the native script of its language (for Russian, Arabic, and Kannada) and translating between a pair of closely related languages (Serbian and Bosnian).Finally, we investigate how combining finite-state and sequence-to-sequence models at decoding time affects the output quantitatively and qualitatively. 1
Factors Influencing the Software Development Process in Small Scale Industries

Software development is a complex process which is divided into many phases. According to the software type and industries the development process is restructured. During the entire development what are the main factors which is influencing the process and affecting the quality. The main objective of this study is to focus on factors influencing the development process and how it affects the small scale industries after coming in to the real practice.&#x0D; Entire Software development is a layered process in which different factors are responsible to get the best products. This paper is focused on different technical and non-technical influencing factors which give major impact on the software quality. With influencing factors, their applicability in small scale industries also studied.&#x0D; Three important technical factors i.e. SDLC model and its principles, Cost estimation and Risk parameter whereas two important influencing factors in non-technical.i.e. success factors and environmental factors. Non-technical factors more influencing than technical factors. &#x0D; All technical and non-technical factors have their own role but to apply all these quality parameters in small scale industries we need to make them more easy for their applicability. If quality development process and its parameters are tuned to easy and affordable level more businessmen will shift from manual working environment to the digital working environment.
Watch that seam! Designing hybrid presentations with data visualisation in augmented reality

Presentations with Augmented Reality (AR) are becoming an essential tool of design teams. They allow designers to more easily connect the dots leading to design ideas. However, data visualisation with AR in the context of presentations are little researched to date. This leaves two key research questions: (1) How to design an AR data visualisation in presentations? and (2) How to orchestrate seams in presentations that include AR? In order to answer these, we report an in-depth Research through Design (RtD) case study, exploring the design process of two data visualisation prototypes in HoloLens. This research process highlights the implications of including a data visualisation in AR in the presentation structure, generating the appearance of seams or transitions between media channels. Based on our findings, we make three contributions. First, we propose the concept of hybrid presentations: as a type of presentation that blends, in the same narrative, at least two media channels that combine physical and virtual media. Second, we explain how seams can be orchestrated in hybrid presentations by introducing the idea of an interaction between channel seams and narrative seams. Third, we translate these insights into concrete guidelines for designers dealing with hybrid presentations and design of data visualisations in AR.
Simultaneously Self-Attending to Text and Entities for Knowledge-Informed Text Representations

Pre-trained language models have emerged as highly successful methods for learning good text representations.However, the amount of structured knowledge retained in such models, and how (if at all) it can be extracted, remains an open question.In this work, we aim at directly learning text representations which leverage structured knowledge about entities mentioned in the text.This can be particularly beneficial for downstream tasks which are knowledge-intensive.Our approach utilizes self-attention between words in the text and knowledge graph (KG) entities mentioned in the text.While existing methods require entity-linked data for pre-training, we train using a mention-span masking objective and a candidate ranking objective -which doesn't require any entity-links and only assumes access to an alias table for retrieving candidates, enabling large-scale pre-training.We show that the proposed model learns knowledgeinformed text representations that yield improvements on the downstream tasks over existing methods.
Penulisan Artikel dan Strategi Publikasi di Jurnal Nasional dan Internasional: Usaha Internasionalisasi Perguruan Tinggi

Menulis di jurnal nasional bereputasi dan internasional bereputasi merupakan salah satu cara meningkatkan kolaborasi dan koneksi antarpeniliti, namun masih dianggap sebagai sesuatu yang sulit untuk dilakukan. Pengabdian ini bertujuan untuk membantu peneliti (dosen pemula dan mahasiswa pascasarjana) menulis dan mempublikasikan artikel di jurnal nasional maupun internasional bereputasi secara efektif. Pengabdian ini menerapkan metode Virtual-Based Active Participatory Action. Peserta yang terlibat dalam kegiatan ini berjumlah 332 peserta melalui virtual meeting. 30 peserta terlibat dalam intensive meeting dan didesain dalam sesi Article Schools. Peserta intensive meeting dibatasi berdasarkan kualitas artikel yang direview oleh tim pengabdian. Berdasarkan hasil pelaksanaan pengabdian ini, bahwa dosen pemula dan mahasiswa pascasarjana dapat mengatasi kesulitan dalam menentukan kebaharuan (novelty) penelitian, topik atau isu-isu penelitian yang up-to-date secara nasional dan internasional, mempresentasikan review of related literature dalam artikel dengan baik, dan menentukan jurnal tempat publikasi yang tepat. Article Writing and Publication Strategies in National and International Journals: Efforts to Internationalize Higher Education Abstract Writing in reputable national and international journals is one of techniques to improve authors or researchers’ collaboration and connection but it is considered as a uneasy action to be done. This dedication aims to help researchers (novice lecturers and postgraduate students) write and publish articles in reputable national and international journals effectively. This service applies the Virtual-Based Active Participatory Action method. Participants involved in the service totaled 332 participants in virtual meetings. Thirty participants were engaged in intensive meetings and were designed in Article Schools sessions. Participants in this intensive meeting are limited based on the quality of the articles reviewed by the service team. Based on the results of this service, the findings show that the novice lecturers and the postgraduate students can diminish their difficulties in determining the novelty of their studies; their topics or research issues are up-to-date nationally and internationally; they overcome their problems in presenting reviews of related literature in articles; and determine the reputable publication journals.
Continual Learning for Object Classification: A Modular Approach

A Human can immediately add new items to its set of known objects, whereas a computer, using traditional computer vision algorithms, would typically have to go almost back to the start and re-learn the all collection of objects (classes) from scratch. The reason the network must be re-trained is due to a phenomenon named Catastrophic Forgetting, where the changes made to the system during the acquisition of new knowledge brings about the loss of previous knowledge. In this paper, we explore the Continual Learning problem by proposing a way to deal with Catastrophic Forgetting. Our proposal includes a framework capable of learning new information without having to start from scratch and even “improve” its knowledge on what it already knows. With the above in mind, we present the Modular Dynamic Neural Network (MDNN), a network primarily made up of modular sub-networks that progressively grows in a tree shape and re-arranges itself as it learns continuously. The network is divided into two main blocks: (a) the feature extraction block, which is based on a ResNet50; and (b) the modular dynamic classification block, which is made up of sub-networks structured in such a way that its internal components function independently from one another. This structure allows that when new information is learned only specific sub-networks are altered in a way that old information is not forgotten. Tests show promising results with a set of ImageNet classes and also with a set of our own classes.
Endless fun in high dimensions—A quantum card game

Quantum technologies enable new ways to distribute and process information. The enormous progress over the recent decades has led to an urgent need for new educational programs to train professionals to work in this field. Here, we present a card game that teaches students the building blocks of quantum computing through strategic gameplay. Participants start from the lowest quantum state and play cards that change their state and/or their opponents' state, aiming to build an algorithm that achieves the highest possible quantum state. Players can utilize several different strategies that rely on quantum features such as randomness, superposition, interference, and entanglement. Our game expands on the existing Q|Cards⟩ game, originally developed using traditional qubits (with 2-level states), by including an option to play with qutrits (with 3-level states), and by developing cooperative and single player modes in addition to the existing competitive mode. The presented game contributes to the ongoing efforts on gamifying quantum physics education with a particular focus on the counter-intuitive features that make quantum computing powerful.
Peningkatkan Kemampuan Berhitung Permulaan Melalui Media Sempoa

The purpose of this study was to describe the increase in numeracy skills of 0-10 through the Abacus Media for Group B children in Integrated PAUD Nagallo Marannu, Puncak Jaya Regency, Academic Year 2017/2018. This research was conducted on group B children, amounting to 16 people. This research is an action research with the method of Kemmis and Taggart which consists of 4 stages, namely (plan, act, observe and reflect). This study consisted of 2 cycles, each of which consisted of 3 meetings. Data collection techniques using observation, field notes, and documentation. Data analysis using quantitative and qualitative. Quantitative data analysis was performed with statistical descriptions to compare pre-cycle to cycle II. Qualitative analysis is carried out by analyzing data from observations, field notes and verified documentation. The results showed that there was an increase in the ability to count numbers 0-10 through the abacus media with a score in the pre-cycle of 46.35% increasing to 66.66% in the first cycle and an increase of 82.29 in the second cycle with the very well developed category.