Reviewerly/exHarmony huggingface dataset:
Sign language recognition using Kinect sensor based on color stream and skeleton points

This paper presents a sign language recognition system based on color stream and skeleton points. Several approaches have been established to address sign language recognition problems. However, most of the previous approaches still have poor recognition accuracy. The proposed approach uses Kinect sensor based on color stream and skeleton points from the depth stream to improved recognition accuracy. Techniques within this approach use hand trajectories and hand shapes in combating sign recognition challenges. Therefore, for a particular sign a representative feature vector is extracted, which consists of hand trajectories and hand shapes. A sparse dictionary learning algorithm, Label Consistent K-SVD (LC-KSVD) is applied to obtain a discriminative dictionary. Based on that, the system was further developed to a new classification approach for better results. The proposed system was fairly evaluated based on 21 sign words including one-handed signs and two-handed signs. It was observed that the proposed system gets high recognition accuracy of 98.25%, and obtained an average accuracy of 95.34% for signer independent recognition.&#x0D; Keywords: Sign language, Color stream, Skeleton points, Kinect sensor, Discriminative dictionary.
A Joint Model for Structure-based News Genre Classification with Application to Text Summarization

Journalists usually organize and present the contents of a news article following a welldefined structure.In this paper, we propose a novel joint model for structure-based news genre classification that simultaneously identifies one of four commonly used news structures (including Inverted Pyramid and three other structures) for a news article as well as recognizes a sequence of news elements within the article that define the corresponding news structure.Experiments show that the joint model consistently outperforms its variants that perform two tasks independently, which supports our motivation that preserving the two-way dependencies and constraints between a type of news structure and its sequence of news elements enables the model to better predict both of them.Although being not perfect, the system predicted news structure type and news elements have improved the performance of text summarization when incorporated into a recent neural network system.
Content Learning with Structure-Aware Writing: A Graph-Infused Dual Conditional Variational Autoencoder for Automatic Storytelling

Recent automatic storytelling methods mainly rely on keyword planning or plot skeleton generation to model long-range dependencies and create consistent narrative texts. However, these approaches generate story plans or plots sequentially, leaving the non-sequential conception and structural design processes of human writers unexplored. To mimic human writers and exploit the fine-grained, intrinsic structural information of each story, we decompose automatic story generation into sub-problems of graph construction, graph generation, and graph-infused sequence generation. Specifically, we propose a graph-infused dual conditional variational autoencoder model to capture multi-level intra-story structures (i.e., graph) by continuous variational latent variables and generate consistent stories through dual-infusion of story structure planning and content learning. Experimental results on the ROCStories dataset and the CMU Movie Summary corpus confirm that our proposed model outperforms strong baselines in both human judges and widely-used automatic metrics.
Building manifolds from quantum codes

We give a procedure for "reverse engineering" a closed, simply connected, Riemannian manifold with bounded local geometry from a sparse chain complex over $${\mathbb {Z}}$$ . Applying this procedure to chain complexes obtained by "lifting" recently developed quantum codes, which correspond to chain complexes over $${\mathbb {Z}}_2$$ , we construct the first examples of power law $${\mathbb {Z}}_2$$ systolic freedom. As a result that may be of independent interest in graph theory, we give an efficient randomized algorithm to construct a weakly fundamental cycle basis for a graph, such that each edge appears only polylogarithmically times in the basis. We use this result to trivialize the fundamental group of the manifold we construct.
Erase and Rewind: Manual Correction of NLP Output through a Web Interface

In this paper, we present Tintful, an NLP annotation software that can be used both to manually annotate texts and to fix mistakes in NLP pipelines, such as Stanford CoreNLP.Using a paradigm similar to wiki-like systems, a user who notices some wrong annotation can easily fix it and submit the resulting (and right) entry back to the tool developers.Moreover, Tintful can be used to easily annotate data from scratch.The input documents do not need to be in a particular format: starting from the plain text, the sentences are first annotated with CoreNLP, then the user can edit the annotations and submit everything back through a user-friendly interface.A video showing Tintful and its feature is available on YouTube. 1
IBM Scenario Planning Advisor: A Neuro-Symbolic ERM Solution

Scenario Planning is a commonly used Enterprise Risk Management (ERM) technique to help decision makers with longterm plans by considering multiple alternative futures. It is typically a manual, highly labor intensive process involving dozens of experts and hundreds to thousands of person-hours. We previously introduced a Scenario Planning Advisor prototype (Sohrabi et al. 2018a,b) that focuses on generating scenarios quickly based on expert-developed models. We present the evolution of that prototype into a full-scale, cloud deployed ERM solution that: (i) can automatically (through NLP) create models from authoritative documents such as books, reports and articles, such that what typically took hundreds to thousands of person-hours can now be achieved in minutes to hours; (ii) can gather news and other feeds relevant to forces in the risk models and group them into storylines without any other user input; (iii) can generate scenarios at scale, starting with dozens of forces of interest from models with thousands of forces in seconds; (iv) provides interactive visualizations of scenario and force model graphs, including a full model editor in the browser. The SPA solution is deployed under a non-commercial use license at https://spa-service.draco.res.ibm.com and includes a user guide to help new users get started. A video demonstration is available at https://www.youtube.com/watch?v=IaX3d37NUl8.
Location Based Fake News Detection using Machine Learning

Now a days lots of crime news incremented. In this system we can easy find which type of crime happened in particular city by using pin code. The easy access and exponential growth of the information available on social media network has made it detect to news or information fake or not. The easy dissemination of shared information has added to exponential growth of its falsification. On social media spreading lots of fake news. Thus it has become research challenge to automatically check detected news or information fake or real. Machine learning plays important role to classify the information in different categories. This paper reviews finding different types of crime news in particular city and detected news fake or real. One more feature is predict the future of crimes.
Detection of Digital Manipulation in Facial Images (Student Abstract)

Advances in deep learning have enabled the creation of photo-realistic DeepFakes by switching the identity or expression of individuals. Such technology in the wrong hands can seed chaos through blackmail, extortion, and forging false statements of influential individuals. This work proposes a novel approach to detect forged videos by magnifying their temporal inconsistencies. A study is also conducted to understand role of ethnicity bias due to skewed datasets on deepfake detection. A new dataset comprising forged videos of Indian ethnicity individuals is presented to facilitate this study.
New Generation Teacher: Challenges And Technologies

At present higher educational institutions are integrating into the global educational space.In this research, the authors aim to identify the conditions for the formation of a new type of teacher: teacher-facilitator, teacher-tutor, teacher-moderator in the educational process for all levels of Higher Education: Bachelor's / Master's Degree Programmes based on the data obtained at Sevastopol State University.The characteristics of the main components of the teacher-facilitator are distinguished.The relevance of the problem investigated in the article is due to modern trends in the study of the methods of using e-learning and distance learning technologies in the educational process for the teachers' professional competencies formation.The term «online identity» is considered in the theoretical aspect.The factors of its formation are highlighted.Based on the analysis of literary sources and the conducted empirical study, the authors present and substantiate the recommendation for online educators about the sorts of skills that it will be worth them gaining in order to be prepared for future innovations in e-learning.The study also presents the results of the experiment to reveal the attitude of future teachers to the expansion of professional roles and to identify the greatest benefit for creating an online identity as an educator in teaching to conduct professional training of undergraduate students.The new educator's skills are summarized.
How RL Agents Behave When Their Actions Are Modified

Reinforcement learning in complex environments may require supervision to prevent the agent from attempting dangerous actions. As a result of supervisor intervention, the executed action may differ from the action specified by the policy. How does this affect learning? We present the Modified-Action Markov Decision Process, an extension of the MDP model that allows actions to differ from the policy. We analyze the asymptotic behaviours of common reinforcement learning algorithms in this setting and show that they adapt in different ways: some completely ignore modifications while others go to various lengths in trying to avoid action modifications that decrease reward. By choosing the right algorithm, developers can prevent their agents from learning to circumvent interruptions or constraints, and better control agent responses to other kinds of action modification, like self-damage.
Efficient Binarized Convolutional Layers for Visual Inspection Applications on Resource-Limited FPGAs and ASICs

There has been a recent surge in publications related to binarized neural networks (BNNs), which use binary values to represent both the weights and activations in deep neural networks (DNNs). Due to the bitwise nature of BNNs, there have been many efforts to implement BNNs on ASICs and FPGAs. While BNNs are excellent candidates for these kinds of resource-limited systems, most implementations still require very large FPGAs or CPU-FPGA co-processing systems. Our work focuses on reducing the computational cost of BNNs even further, making them more efficient to implement on FPGAs. We target embedded visual inspection tasks, like quality inspection sorting on manufactured parts and agricultural produce sorting. We propose a new binarized convolutional layer, called the neural jet features layer, that learns well-known classic computer vision kernels that are efficient to calculate as a group. We show that on visual inspection tasks, neural jet features perform comparably to standard BNN convolutional layers while using less computational resources. We also show that neural jet features tend to be more stable than BNN convolution layers when training small models.
Creating Character Networks with Kinship Relations from Comics

To find and understand comic contents, we present an approach of creating character networks with semantic relations from comics using the panels and speech balloons. First, we build a dataset that contains a comic’s characters and its words in speech balloons separated by the panels. Second, we extract the semantic relations among characters using the words in the speech balloons and panels. Third, we develop character networks by identifying the important characters using the frequencies of characters and their relatedness, and then we add semantic relations to these networks. As an initial step in finding semantic relations, we focus on kinship relations. We implement our approach and successfully build character networks from four popular Japanese comics. Finally, we evaluate our kinship relation extraction algorithms and the created character networks.
A Human-machine Collaborative Framework for Evaluating Malevolence in Dialogues

Conversational dialogue systems (CDSs) are hard to evaluate due to the complexity of natural language.Automatic evaluation of dialogues often shows insufficient correlation with human judgements.Human evaluation is reliable but labor-intensive.We introduce a human-machine collaborative framework, HMCEval, that can guarantee reliability of the evaluation outcomes with reduced human effort.HMCEval casts dialogue evaluation as a sample assignment problem, where we need to decide to assign a sample to a human or a machine for evaluation.HMCEval includes a model confidence estimation module to estimate the confidence of the predicted sample assignment, and a human effort estimation module to estimate the human effort should the sample be assigned to human evaluation, as well as a sample assignment execution module that finds the optimum assignment solution based on the estimated confidence and effort.We assess the performance of HMCEval on the task of evaluating malevolence in dialogues.The experimental results show that HMCEval achieves around 99% evaluation accuracy with half of the human effort spared, showing that HMCEval provides reliable evaluation outcomes while reducing human effort by a large amount.
Benchmarking Scalable Methods for Streaming Cross Document Entity Coreference

Streaming cross document entity coreference (CDC) systems disambiguate mentions of named entities in a scalable manner via incremental clustering.Unlike other approaches for named entity disambiguation (e.g., entity linking), streaming CDC allows for the disambiguation of entities that are unknown at inference time.Thus, it is well-suited for processing streams of data where new entities are frequently introduced.Despite these benefits, this task is currently difficult to study, as existing approaches are either evaluated on datasets that are no longer available, or omit other crucial details needed to ensure fair comparison.In this work, we address this issue by compiling a large benchmark adapted from existing free datasets, and performing a comprehensive evaluation of a number of novel and existing baseline models. 1 We investigate: how to best encode mentions, which clustering algorithms are most effective for grouping mentions, how models transfer to different domains, and how bounding the number of mentions tracked during inference impacts performance.Our results show that the relative performance of neural and feature-based mention encoders varies across different domains, and in most cases the best performance is achieved using a combination of both approaches.We also find that performance is minimally impacted by limiting the number of tracked mentions.
A Real-Time Business Analysis Framework Using Virtual Data Warehouse

Data Warehouse (DW) is widely used in industries over decades to perform the analysis on data to expedite decision-making process.However, the traditional DW is slower in execution due to the huge time overhead of pre-processing stages of Extraction-Transformation-Loading (ETL).On the other hand, often the situations arise where the decision-making are required in real time.Data virtualization is one of the robust approaches over traditional data warehouse that avoids the costly steps of ETL processing.Virtual Data Warehouse (VDW) allows specific analysis for quick decision making even on the unprocessed data.Moreover, VDW could be used by the organizations that maintain DW to take some immediate business decisions for some abrupt changes.This research work performs business trend specific analysis based on VDW to generate business intelligence even in the catastrophic situations.Experimental results reveal, the proposed methodology based on VDW is around thousand times faster than traditional warehouses.
Climbing the Tower of Treebanks: Improving Low-Resource Dependency Parsing via Hierarchical Source Selection

Recent work on multilingual dependency parsing focused on developing highly multilingual parsers that can be applied to a wide range of low-resource languages.In this work, we substantially outperform such "one model to rule them all" approach with a heuristic selection of languages and treebanks on which to train the parser for a specific target language.Our approach, dubbed TOWER, first hierarchically clusters all Universal Dependencies languages based on their mutual syntactic similarity computed from human-coded URIEL vectors.For each low-resource target language, we then climb this language hierarchy starting from the leaf node of that language and heuristically choose the hierarchy level at which to collect training treebanks.This treebank selection heuristic is based on: (i) the aggregate size of all treebanks subsumed by the hierarchy level and (ii) the similarity of the languages in the training sample with the target language.For languages without development treebanks, we additionally use (ii) for model selection (i.e., early stopping) in order to prevent overfitting to development treebanks of closest languages.Our TOWER approach shows substantial gains for low-resource languages over two state-ofthe-art multilingual parsers, with more than 20 LAS point gains for some of those languages.Parsing models and code available at: https: //github.com/codogogo/towerparse.
Writing Polishment with Simile: Task, Dataset and A Neural Approach

A simile is a figure of speech that directly makes a comparison, showing similarities between two different things, e.g. ``Reading papers can be dull sometimes,like watching grass grow". Human writers often interpolate appropriate similes into proper locations of the plain text to vivify their writings. However, none of existing work has explored neural simile interpolation, including both locating and generation. In this paper, we propose a new task of Writing Polishment with Simile (WPS) to investigate whether machines are able to polish texts with similes as we human do. Accordingly, we design a two-staged Locate&amp;Gen model based on transformer architecture. Our model firstly locates where the simile interpolation should happen, and then generates a location-specific simile. We also release a large-scale Chinese Simile (CS) dataset containing 5 million similes with context. The experimental results demonstrate the feasibility of WPS task and shed light on the future research directions towards better automatic text polishment.
The DHCP Snooping and DHCP Alert Method in Securing DHCP Server from DHCP Rogue Attack

DHCP Server as part of the network infrastructure in charge of distributing host configurations to all devices has the potential to be controlled. If the DHCP Server is successfully controlled, all network devices connected to the server can potentially be controlled. From the observations made at PT. Rekayasa Engineering found a vulnerability in the DHCP Server that has the potential to experience DHCP Rogue or DHCP Spoofing, where the client will fail to communicate with the authorized DHCP Server, as well as open the door for attackers to enter the network. For this reason, DHCP Snooping and DHCP Alert methods are implemented. DHCP Snooping will ensure that every data traffic has been filtered and directed to the registered interface. Meanwhile, the use of DHCP Alert is required in monitoring data traffic during the Discover, Offer, Request, and Acknowledge (DORA) process. In the tests performed, DHCP Snooping and DHCP Alert managed to anticipate attacks that tried to placed DHCP Rogue on the network infrastructure. DHCP Alert, configured on the proxy router, ensures that the DORA process can only occur between an authorized DHCP server and a client. DHCP Snooping test also shows that communication from clients can only be replied to by Trusted DHCP Server. The existence of DHCP Snooping and DHCP Alert makes the host configuration fully controlled by the authorized DHCP Server.
MergeDistill: Merging Language Models using Pre-trained Distillation

Pre-trained multilingual language models (LMs) have achieved state-of-the-art results in cross-lingual transfer, but they often lead to an inequitable representation of languages due to limited capacity, skewed pre-training data, and sub-optimal vocabularies.This has prompted the creation of an ever-growing pretrained model universe, where each model is trained on large amounts of language or domain specific data with a carefully curated, linguistically informed vocabulary.However, doing so brings us back full circle and prevents one from leveraging the benefits of multilinguality.To address the gaps at both ends of the spectrum, we propose MERGEDISTILL, a framework to merge pre-trained LMs in a way that can best leverage their assets with minimal dependencies, using task-agnostic knowledge distillation.We demonstrate the applicability of our framework in a practical setting by leveraging pre-existing teacher LMs and training student LMs that perform competitively with or even outperform teacher LMs trained on several orders of magnitude more data and with a fixed model capacity.We also highlight the importance of teacher selection and its impact on student model performance.
Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations

Text-based games (TBGs) have emerged as useful benchmarks for evaluating progress at the intersection of grounded language understanding and reinforcement learning (RL).Recent work has proposed the use of external knowledge to improve the efficiency of RL agents for TBGs.In this paper, we posit that to act efficiently in TBGs, an agent must be able to track the state of the game while retrieving and using relevant commonsense knowledge.Thus, we propose an agent for TBGs that induces a graph representation of the game state and jointly grounds it with a graph of commonsense knowledge from ConceptNet.This combination is achieved through bidirectional knowledge graph attention between the two symbolic representations.We show that agents that incorporate commonsense into the game state graph outperform baseline agents.Observation: You've entered a kitchen.You see a dishwasher and a fridge.Here's a dining table.You see a red apple and a dirty plate on the table.