Reviewerly/exHarmony huggingface dataset:
Decision support system with MOORA method in selection of the best teachers

This study aims to build a decision support system that can help make it easier to evaluate employees and teachers at the Tamansiswa Private High School Pematangsiantar branch. Every employee in the organization is required to provide positive performance through good performance, considering that organizational performance depends on the performance of its employees. For this reason, an objective assessment of employee performance will be very useful to motivate employees to work well. To simplify the process of evaluating employee performance that occurs at the Tamansiswa Private High School, it is done by designing a Decision Support System using the MOORA (Multi-Objective Optimization on the Basis of Ratio Analysis) method. This DSS uses criteria consisting of Creativity, Initiative, Personality, Social, Professionalism, and the number of absences. The results of this study obtained recommendations for the best teacher, namely the teacher with an alternative Tamsis_teacher_01 with a Yi value of 0,39105.
Decision support system for selection of food aid recipients using SAW method

This research was conducted to overcome the problem of determining the recipients of non-cash food assistance in the village of Nagori Bayu Bagasan. Assistance in the form of non-cash food assistance is one of the efforts provided by the government to reduce the burden of spending on the poor. In the selection of the recipients of non-cash food assistance in the village of Nagori Bayu Bagasan, they still did not use a decision support system approach so that the recipients of the non-cash food assistance were not on target. To overcome this, a Decision Support System was designed using the SAW method to assist the selection of recipients of non-cash food aid assistance. This Decision Support System is designed with 5 criteria, namely employment, income, dependents, homeownership, and housing conditions. Through the application of the SAW method, the selection of the best non-cash food aid recipients is obtained from the results carried out by ranking (ranking). The results of the SPK SAW method in this study were obtained that, 5 samples of people who were eligible to receive non-cash food assistance were with priority values, including 85.5; 77.5; 72; 71; 67.
A Fusional Cubic-Sine Map Model for Secure Medical Image Transmission

Medical images are one of the moset significant attribute for diagnoising the disease in medical systems. In today modernization in digital environment medical images are hacked during transmission on insecure network. By considering the patient’s privacy and security their medical images has to be transferred in secure maner. This work aims for proposing a new medical image cipher architecture by based on fusional chaotic map. At first, a fusional Cubic-Sine Map (CSM) is proposed to generate pseudorandom numbers, then confusion and diffusion of image is executed based on the chaotic series produced by CSM. Experimental results and security analysis indicate that the developed chaotic map model generate sufficient random series and also the proposed cipher model has the ability to resisit Statistical, exhaustive, crop and noise attacks attacks.
A Study on Surveillance System Using Deep Learning Methods

Video Surveillance data is one of the varieties of Big Data as it produces a huge amount of data required for further needs. Nowadays, the number of disruptive and aggressive activities that have been occuring are increased dramatically. Hence, securing individuals in public places like shopping malls, banks, public transportations, etc., has become significant. These public places are being equipped with CCTV (Closed Circuit Television) cameras to monitor the activity of the people. Monitoring also needs human’s consistent focus to analyze the captured scenes and to react immediately if there is any suspicious activity such as theft, sabotage, bullying, etc. But constant focus on surveillance camera videos and identifying the unusual activities in the video is a challenging task as it needs time and manpower. Hence this paper analyzes various start-of-the-art video analytics methods to detect any aggression and unusual sign.
Understanding Politics via Contextualized Discourse Processing

Politicians often have underlying agendas when reacting to events. Arguments in contexts of various events reflect a fairly consistent set of agendas for a given entity. In spite of recent advances in Pretrained Language Models (PLMs), those text representations are not designed to capture such nuanced patterns. In this paper, we propose a Compositional Reader model consisting of encoder and composer modules, that attempts to capture and leverage such information to generate more effective representations for entities, issues, and events. These representations are contextualized by tweets, press releases, issues, news articles, and participating entities. Our model can process several documents at once and generate composed representations for multiple entities over several issues or events. Via qualitative and quantitative empirical analysis, we show that these representations are meaningful and effective.
ECONET: Effective Continual Pretraining of Language Models for Event  Temporal Reasoning

While pre-trained language models (PTLMs) have achieved noticeable success on many NLP tasks, they still struggle for tasks that require event temporal reasoning, which is essential for event-centric applications. We present a continual pre-training approach that equips PTLMs with targeted knowledge about event temporal relations. We design self-supervised learning objectives to recover masked-out event and temporal indicators and to discriminate sentences from their corrupted counterparts (where event or temporal indicators got replaced). By further pre-training a PTLM with these objectives jointly, we reinforce its attention to event and temporal information, yielding enhanced capability on event temporal reasoning. This effective continual pre-training framework for event temporal reasoning (ECONET) improves the PTLMs' fine-tuning performances across five relation extraction and question answering tasks and achieves new or on-par state-of-the-art performances in most of our downstream tasks.
Building domain specific lexicon based on TikTok comment dataset

In the sentiment analysis task, predicting the sentiment tendency of a sentence is an important branch. Previous research focused more on sentiment analysis in English, for example, analyzing the sentiment tendency of sentences based on Valence, Arousal, Dominance of sentences. the emotional tendency is different between the two languages. For example, the sentence order between Chinese and English may present different emotions. This paper tried a method that builds a domain-specific lexicon. In this way, the model can classify Chinese words with emotional tendency. In this approach, based on the [13], an ultra-dense space embedding table is trained through word embedding of Chinese TikTok review and emotional lexicon sources(seed words). The result of the model is a domain-specific lexicon, which presents the emotional tendency of words. I collected Chinese TikTok comments as training data. By comparing The training results with the PCA method to evaluate the performance of the model in Chinese sentiment classification, the results show that the model has done well in Chinese. The source code has released on github:https://github.com/h2222/douyin_comment_dataset
A comparison of self-supervised speech representations as input features  for unsupervised acoustic word embeddings

Many speech processing tasks involve measuring the acoustic similarity between speech segments. Acoustic word embeddings (AWE) allow for efficient comparisons by mapping speech segments of arbitrary duration to fixed-dimensional vectors. For zero-resource speech processing, where unlabelled speech is the only available resource, some of the best AWE approaches rely on weak top-down constraints in the form of automatically discovered word-like segments. Rather than learning embeddings at the segment level, another line of zero-resource research has looked at representation learning at the short-time frame level. Recent approaches include self-supervised predictive coding and correspondence autoencoder (CAE) models. In this paper we consider whether these frame-level features are beneficial when used as inputs for training to an unsupervised AWE model. We compare frame-level features from contrastive predictive coding (CPC), autoregressive predictive coding and a CAE to conventional MFCCs. These are used as inputs to a recurrent CAE-based AWE model. In a word discrimination task on English and Xitsonga data, all three representation learning approaches outperform MFCCs, with CPC consistently showing the biggest improvement. In cross-lingual experiments we find that CPC features trained on English can also be transferred to Xitsonga.
Understanding Action Sequences based on Video Captioning for  Learning-from-Observation

Learning actions from human demonstration video is promising for intelligent robotic systems. Extracting the exact section and re-observing the extracted video section in detail is important for imitating complex skills because human motions give valuable hints for robots. However, the general video understanding methods focus more on the understanding of the full frame,lacking consideration on extracting accurate sections and aligning them with the human's intent. We propose a Learning-from-Observation framework that splits and understands a video of a human demonstration with verbal instructions to extract accurate action sequences. The splitting is done based on local minimum points of the hand velocity, which align human daily-life actions with object-centered face contact transitions required for generating robot motion. Then, we extract a motion description on the split videos using video captioning techniques that are trained from our new daily-life action video dataset. Finally, we match the motion descriptions with the verbal instructions to understand the correct human intent and ignore the unintended actions inside the video. We evaluate the validity of hand velocity-based video splitting and demonstrate that it is effective. The experimental results on our new video captioning dataset focusing on daily-life human actions demonstrate the effectiveness of the proposed method. The source code, trained models, and the dataset will be made available.
Improving Gradient Flow with Unrolled Highway Expectation Maximization

Integrating model-based machine learning methods into deep neural architectures allows one to leverage both the expressive power of deep neural nets and the ability of model-based methods to incorporate domain-specific knowledge. In particular, many works have employed the expectation maximization (EM) algorithm in the form of an unrolled layer-wise structure that is jointly trained with a backbone neural network. However, it is difficult to discriminatively train the backbone network by backpropagating through the EM iterations as they are prone to the vanishing gradient problem. To address this issue, we propose Highway Expectation Maximization Networks (HEMNet), which is comprised of unrolled iterations of the generalized EM (GEM) algorithm based on the Newton-Rahpson method. HEMNet features scaled skip connections, or highways, along the depths of the unrolled architecture, resulting in improved gradient flow during backpropagation while incurring negligible additional computation and memory costs compared to standard unrolled EM. Furthermore, HEMNet preserves the underlying EM procedure, thereby fully retaining the convergence properties of the original EM algorithm. We achieve significant improvement in performance on several semantic segmentation benchmarks and empirically show that HEMNet effectively alleviates gradient decay.
Generative Adversarial Network based Heuristics for Sampling-based Path  Planning

Sampling-based path planning is a popular methodology for robot path planning. With a uniform sampling strategy to explore the state space, a feasible path can be found without the complex geometric modeling of the configuration space. However, the quality of initial solution is not guaranteed and the convergence speed to the optimal solution is slow. In this paper, we present a novel image-based path planning algorithm to overcome these limitations. Specifically, a generative adversarial network (GAN) is designed to take the environment map (denoted as RGB image) as the input without other preprocessing works. The output is also an RGB image where the promising region (where a feasible path probably exists) is segmented. This promising region is utilized as a heuristic to achieve nonuniform sampling for the path planner. We conduct a number of simulation experiments to validate the effectiveness of the proposed method, and the results demonstrate that our method performs much better in terms of the quality of initial solution and the convergence speed to the optimal solution. Furthermore, apart from the environments similar to the training set, our method also works well on the environments which are very different from the training set.
An Enhanced MeanSum Method For Generating Hotel Multi-Review  Summarizations

Multi-document summaritazion is the process of taking multiple texts as input and producing a short summary text based on the content of input texts. Up until recently, multi-document summarizers are mostly supervised extractive. However, supervised methods require datasets of large, paired document-summary examples which are rare and expensive to produce. In 2018, an unsupervised multi-document abstractive summarization method(Meansum) was proposed by Chu and Liu, and demonstrated competitive performances comparing to extractive methods. Despite good evaluation results on automatic metrics, Meansum has multiple limitations, notably the inability of dealing with multiple aspects. The aim of this work was to use Multi-Aspect Masker(MAM) as content selector to address the issue with multi-aspect. Moreover, we propose a regularizer to control the length of the generated summaries. Through a series of experiments on the hotel dataset from Trip Advisor, we validate our assumption and show that our improved model achieves higher ROUGE, Sentiment Accuracy than the original Meansum method and also beats/ comprarable/close to the supervised baseline.
Quantum Sampling for Optimistic Finite Key Rates in High Dimensional  Quantum Cryptography

It has been shown recently that the framework of quantum sampling, as introduced by Bouman and Fehr, can lead to new entropic uncertainty relations highly applicable to finite-key cryptographic analyses. Here we revisit these so-called sampling-based entropic uncertainty relations, deriving newer, more powerful, relations and applying them to source-independent quantum random number generators and high-dimensional quantum key distribution protocols. Along the way, we prove several interesting results in the asymptotic case for our entropic uncertainty relations. These sampling-based approaches to entropic uncertainty, and their application to quantum cryptography, hold great potential for deriving proofs of security for quantum cryptographic systems, and the approaches we use here may be applicable to an even wider range of scenarios.
Security for Software on Tiny Devices

At over 9 billion embedded processors in use today, the number of embedded devices has surpassed the number of humans. With the rise of the “Internet of Things” (IoT), the number of embedded devices, their complexity, and their connectivity is exploding. These smart “things” include fitness trackers, smart light bulbs, smart thermostats, Amazon’s Dash Button, utility smart meters, smart locks, and smart TVs.
Response to Cybersecurity Threats of Informational Infrastructure Based on Conceptual Models

Response to the threats of information security in conditions of modern organization with a large infrastructure is an area with emergency loaded intensity of the data usage. For a successful exposure and the prevention of computer attacks the construction of complex models of the events and infrastructure is required. In this work, the question of the applicability of ontological models and reasoning for supporting response process is examined. On the basis of built ontology, practical use cases are demonstrated.
Polygon-Free: Unconstrained Scene Text Detection with Box Annotations

Unlike existing works that employ fully-supervised training with polygon annotations, this study proposes an unconstrained text detection system termed Polygon-free (PF), in which most existing polygon-based text detectors (e.g., PSENet [1]) are trained with only upright bounding box annotations. Our core idea is to transfer knowledge from synthetic data to real data to enhance the supervision information of upright bounding boxes. This is made possible with a simple segmentation network, namely Skeleton Attention Segmentation Network (SASN), that includes three vital components (i.e., channel attention, spatial attention and skeleton attention map) and one soft cross-entropy loss.Experiments demonstrate that the proposed Polygon-free yields surprisingly high-quality pixel-level results with only upright bounding box annotations. For example, without using polygon annotations, PSENet achieves an 80.5% F-score on TotalText (vs. 80.9% of fully supervised counterpart), 31.1% better than training directly with upright bounding box annotations, and saves 80%+ labeling costs.
Privileged Knowledge Distillation for Online Action Detection

Online Action Detection (OAD) in videos is proposed as a per-frame labeling task to address the real-time prediction tasks that can only obtain the previous and current video frames. This paper presents a novel learning-with-privileged based framework for online action detection where the future frames only observable at the training stages are considered as a form of privileged information. Knowledge distillation is employed to transfer the privileged information from the offline teacher to the online student. We note that this setting is different from conventional KD because the difference between the teacher and student models mostly lies in input data rather than the network architecture. We propose Privileged Knowledge Distillation (PKD) which (i) schedules a curriculum learning procedure and (ii) inserts auxiliary nodes to the student model, both for shrinking the information gap and improving learning performance. Compared to other OAD methods that explicitly predict future frames, our approach avoids learning unpredictable unnecessary yet inconsistent visual contents and achieves state-of-the-art accuracy on two popular OAD benchmarks, TVSeries and THUMOS14.
Alignment Restricted Streaming Recurrent Neural Network Transducer

There is a growing interest in the speech community in developing Recurrent Neural Network Transducer (RNN-T) models for automatic speech recognition (ASR) applications. RNN-T is trained with a loss function that does not enforce temporal alignment of the training transcripts and audio. As a result, RNN-T models built with uni-directional long short term memory (LSTM) encoders tend to wait for longer spans of input audio, before streaming already decoded ASR tokens. In this work, we propose a modification to the RNN-T loss function and develop Alignment Restricted RNN-T (Ar-RNN-T) models, which utilize audio-text alignment information to guide the loss computation. We compare the proposed method with existing works, such as monotonic RNN-T, on LibriSpeech and in-house datasets. We show that the Ar-RNN-T loss provides a refined control to navigate the trade-offs between the token emission delays and the Word Error Rate (WER). The Ar-RNN-T models also improve downstream applications such as the ASR End-pointing by guaranteeing token emissions within any given range of latency. Moreover, the Ar-RNN-T loss allows for bigger batch sizes and 4 times higher throughput for our LSTM model architecture, enabling faster training and convergence on GPUs.
An End to End Network Architecture for Fundamental Matrix Estimation

In this paper, we present a novel end-to-end network architecture to estimate fundamental matrix directly from stereo images. To establish a complete working pipeline, different deep neural networks in charge of finding correspondences in images, performing outlier rejection and calculating fundamental matrix, are integrated into an end-to-end network architecture. To well train the network and preserve geometry properties of fundamental matrix, a new loss function is introduced. To evaluate the accuracy of estimated fundamental matrix more reasonably, we design a new evaluation metric which is highly consistent with visualization result. Experiments conducted on both outdoor and indoor data-sets show that this network outperforms traditional methods as well as previous deep learning based methods on various metrics and achieves significant performance improvements.
Panoster: End-to-end Panoptic Segmentation of LiDAR Point Clouds

Panoptic segmentation has recently unified semantic and instance segmentation, previously addressed separately, thus taking a step further towards creating more comprehensive and efficient perception systems. In this paper, we present Panoster, a novel proposal-free panoptic segmentation method for LiDAR point clouds. Unlike previous approaches relying on several steps to group pixels or points into objects, Panoster proposes a simplified framework incorporating a learning-based clustering solution to identify instances. At inference time, this acts as a class-agnostic segmentation, allowing Panoster to be fast, while outperforming prior methods in terms of accuracy. Without any post-processing, Panoster reached state-of-the-art results among published approaches on the challenging SemanticKITTI benchmark, and further increased its lead by exploiting heuristic techniques. Additionally, we showcase how our method can be flexibly and effectively applied on diverse existing semantic architectures to deliver panoptic predictions.
Can We Enable the Drone to be a Filmmaker?

Drones are enabling new forms of cinematography. However, quadrotor cinematography requires accurate comprehension of the scene, technical skill of flying, artistic skill of composition and simultaneous realization of all the requirements in real time. These requirements could pose real challenge to drone amateurs because unsuitable camera viewpoint and motion could result in unpleasing visual composition and affect the target's visibility. In this paper, we propose a novel autonomous drone camera system which captures action scenes using proper camera viewpoint and motion. The key novelty is that our system can dynamically generate smooth drone camera trajectory associated with human movement while obeying visual composition principles. We evaluate the performance of our cinematography system on simulation and real scenario. The experimental results demonstrate that our system can capture more expressive video footage of human action than that of the state-of-the-art drone camera system. To the best of our knowledge, this is the first cinematography system that enables people to leverage the mobility of quadrotor to autonomously capture high-quality footage of action scene based on subject's movements.