Reviewerly/exHarmony huggingface dataset:
Degradation of phenol in batik industry wastewater using thin layer TiO<sub>2</sub>photocatalyst

Abstract The synthesis of thin layer TiO 2 photocatalyst has been performed and its performance was tested to the degradation of phenol in wastewater of batik industry. The powder of anatase TiO2 was impregnated in glass plate, so that thin layer TiO 2 photocatalyst was obtained. The performance test of thin-layer TiO 2 photocatalyst in the degradation of phenol was performed in a batch photoreactor equipped with six types of black light UV lamp (@10 watts) and a magnetic stirrer. This research has been performed at pH variation and UV radiation time. The result of this research showed that the initial concentration of phenol in batik wastewater was 3,3684 ppm. The optimum pH was obtained at pH 11 with percent degradation of 40.41%. The optimum UV radiation was obtained at 8 hours with percent degradation of 41.72%. The longer the irradiation time the more photon energy is absorbed by TiO 2 causes more positive holes to be formed. The positive holes will react with H 2 O or hydroxyl ions to form OH radicals that further break down organic compounds in waste.
Analyzing Subword Techniques to Improve English to Sinhala Neural Machine Translation

Neural machine translation (NMT) is a remarkable approach which performs much better than the Statistical machine translation (SMT) models when there is an abundance of parallel corpus. However, vanilla NMT is primarily based upon word-level with a fixed vocabulary. Therefore, low resource morphologically rich languages such as Sinhala are mostly affected by the out of vocabulary (OOV) and Rare word problems. Recent advancements in subword techniques have opened up opportunities for low resource communities by enabling open vocabulary translation. In this paper, we extend our recently published state-of-the-art EN-SI translation system using the transformer and explore standard subword techniques on top of it to identify which subword approach has a greater effect on English Sinhala language pair. Our models demonstrate that subword segmentation strategies along with the state-of-the-art NMT can perform remarkably when translating English sentences into a rich morphology language regardless of a large parallel corpus.
Flow-guided Spatial Attention Tracking for Egocentric Activity Recognition

The popularity of wearable cameras has opened up a new dimension for egocentric activity recognition. While some methods introduce attention mechanisms into deep learning networks to capture fine-grained hand-object interactions, they often neglect exploring the spatio-temporal relationships. Generating spatial attention, without adequately exploiting temporal consistency, will result in potentially sub-optimal performance in the video-based task. In this paper, we propose a flow-guided spatial attention tracking (F-SAT) module, which is based on enhancing motion patterns and inter-frame information, to highlight the discriminative features from regions of interest across a video sequence. A new form of input, namely the optical-flow volume, is presented to provide informative cues from moving parts for spatial attention tracking. The proposed F-SAT module is deployed to a two-branch-based deep architecture, which fuses complementary information for egocentric activity recognition. Experimental results on three egocentric activity benchmarks show that the proposed method achieves state-of-the-art performance.
Accounting for Natural Uncertainty Within Monitoring Systems for Induced Seismicity Based on Earthquake Magnitudes

To reduce the probability of future large earthquakes, traffic light systems (TLSs) define appropriate reactions to observed induced seismicity depending on each event's range of local earthquake magnitude ( M L ). The impact of velocity uncertainties and station site effects may be greater than a whole magnitude unit of M L , which can make the difference between a decision to continue (“green” TLS zone) and an immediate stop of operations (“red” zone). We show how to include these uncertainties in thresholds such that events only exceed a threshold with a fixed probability. This probability can be set by regulators to reflect their tolerance to risk. We demonstrate that with the new TLS, a red-light threshold would have been encountered earlier in the hydraulic fracturing operation at Preston New Road, UK, halting operations and potentially avoiding the later large magnitude events. It is therefore critical to establish systems which permit regulators to account for uncertainties when managing risk.
UserReg: A Simple but Strong Model for Rating Prediction

Collaborative filtering (CF) has achieved great success in the field of recommender systems. In recent years, many novel CF models, particularly those based on deep learning or graph techniques, have been proposed for a variety of recommendation tasks, such as rating prediction and item ranking. These newly published models usually demonstrate their performance in comparison to baselines or existing models in terms of accuracy improvements. However, others have pointed out that many newly proposed models are not as strong as expected and are outperformed by very simple baselines.This paper proposes a simple linear model based on Matrix Factorization (MF), called UserReg, which regularizes users’ latent representations with explicit feedback information for rating prediction. We compare the effectiveness of UserReg with three linear CF models that are widely-used as baselines, and with a set of recently proposed complex models that are based on deep learning or graph techniques. Experimental results show that UserReg achieves over-all better performance than the fine-tuned baselines considered and is highly competitive when compared with other recently proposed models. We conclude that UserReg can be used as a strong baseline for future CF research.
Classification of traditional ulos of Batak Toba ssing probabilistic neural network

Abstract Ulos are commonly used in traditional Batak events. Ulos has many variations of pattern and the pattern almost similar to one another. Ulos also have different functions in its use. So, people are often wrong to call the name of Ulos because the similarity of its pattern. Therefore, the research is doing Ulos classification to increase the introduction of Batak Toba’s Ulos. In this research, the method used in classification is Probabilistic Neural Network method. The stages before classification are pre-processing, extraction feature using Gray Level Co-Occurrence Matrix, and then classified using Probabilistic Neural Network. The research uses 650 data to be used as training data and test data. After testing in this research, it was concluded that the Probabilistic Neural Network method had the ability to classify Ulos into five patterns namely Bintang Maratur, Ragi Hidup, Ragi Hotang, Mangiring and Sibolang. The result showed that the accuracy level is 80%.
Dual-Memory Model for Incremental Learning: The Handwriting Recognition Use Case

In this paper, we propose a dual memory model inspired by psychological theory. Short-term memory processes the data stream before integrating them into long-term memory, which generalizes. The use case is learning the ability to recognize handwriting. This begins with the learning of prototypical letters. It continues throughout life and gives the individual the ability to recognize increasingly varied handwriting. This second task is achieved by incrementally training our dual-memory model. We used a convolution network for encoding and random forests as the memory model. Indeed, the latter have the advantage of being easily enhanced to integrate new data and new classes. Performances on the MNIST database are very encouraging since they exceed 95% and the complexity of the model remains reasonable.
Offline Computer-Synthesized Font Character Recognition Using Machine Learning Approaches

The topic of offline character recognition has long been an interesting issue in the field of pattern recognition. Several experiments have shown that the Neural Network works better in image recognition. The core intention of this paper is to incorporate an efficient and reliable technique for offline computer-generated Latin font recognition. The framework uses several machine learning approaches, such as Support for vector machines (SVM), Random Forest (RF), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), like standard classifiers for the recognition. This study compares the performance of these approaches on a benchmark dataset Chars74k with the help of three features extraction techniques. Results show that the deep neural network CNN classifier outperforms the rest without sacrificing performance. The results of these experiments are displayed in various tables and evaluated with the aid of a few evaluation metrics. There are few graphical representations where the rise in the recognition can be confirmed concerning the epoch.
Code-Switch Speech Rescoring with Monolingual Data

In the automatic speech recognition (ASR) system, how to solve the problem of code-switch speech recognition has been a concern. Code-switch speech recognition is challenging due to data scarcity as well as diverse syntactic structures across languages. In this paper, we focus on the code-switch speech recognition in mainland China, which is obviously different from the Hong Kong and Southeast Asia area in linguistic characteristics. We propose a novel approach that only uses monolingual data for code-switch second-pass speech recognition which is also named language model rescoring. The approach converts the code-switch sentence to a monolingual sentence by a word mapping and language model determination step, therefore the issue of data scarcity is unnecessary to be considered. The word pairs during the word mapping step are generated by a fine-designed generation process that incorporates machine translation, word alignment, etc. We show that the proposed approach achieves an over 7.23% relative WER reduction from the naive monolingual language model (MLM) rescoring in our test set.
Content-Aware Speaker Embeddings for Speaker Diarisation

Recent speaker diarisation systems often convert variable length speech segments into fixed-length vector representations for speaker clustering, which are known as speaker embeddings. In this paper, the content-aware speaker embeddings (CASE) approach is proposed, which extends the input of the speaker classifier to include not only acoustic features but also their corresponding speech content, via phone, character, and word embeddings. Compared to alternative methods that leverage similar information, such as multitask or adversarial training, CASE factorises automatic speech recognition (ASR) from speaker recognition to focus on modelling speaker characteristics and correlations with the corresponding content units to derive more expressive representations. CASE is evaluated for speaker re-clustering with a realistic speaker diarisation setup using the AMI meeting transcription dataset, where the content information is obtained by performing ASR based on an automatic segmentation. Experimental results showed that CASE achieved a 17.8% relative speaker error rate reduction over conventional methods.
Image Guided Depth Super-Resolution for Spacewarp in XR Applications

In this paper, an image guided depth super-resolution approach is presented for generating higher-quality and higher-resolution depth maps from lower-resolution depth points, such that it can be used in depth based image re-projection (a.k.a: Spacewarp) and other applications in XR. In this approach, a higher-resolution depth map image is reconstructed using spatial, intensity, and depth information of neighborhood pixels. Three kinds of weights are computed with neighborhood information. The first one uses 3D pose information, the second one uses color image intensity information, and the third one uses color image spatial information. We compute the weighted sum of the depths in neighborhood and accumulate the weights. The depth candidate for the current considered point is determined by the weighted average of the depths in neighborhood. We build a criterion to find an optimal solution for the current pixel. After all pixels are processed, a higher-resolution depth map is obtained.Applications and result analysis for the approach are also presented in this paper. With different levels of down sampled depth points, higher-resolution depth maps are generated with a generally used regular grid interpolation approach and the image guided depth super-resolution algorithm presented in this paper. After comparing and analyzing, we can see that our algorithm can generate a higher-resolution depth map with more accurate and clearer objects and boundaries than generally used interpolation approaches.
Environment and Speaker Related Emotion Recognition in Conversations

ERC (Emotion Recognition in Conversations) is the basis for computers to understand the speakers' sayings and do other downstream tasks, such as chatting and playing music related to the speaker's emotion. However, most approaches are either based on general-purpose language models which fail to exploit crucial information about the environment and the speakers, or resort to complex entanglements of neural network architectures resulting in less stable training procedures and slower inference time. To bridge this gap, we propose a simple and efficient plug-and-play solution based on segment embeddings, leveraging architectural characteristics of recent causal transformers. We obtained near state-of-the-art results in ERC on MELD at a fraction of the usual inference time simply by incorporating information about the speakers and their environments obtained via LDA (Latent Dirichlet Allocation) into DialoGPT, thus without using any additional labels. Empirically, our method yields an F1 score of 63.4 - against current state-of-the-art of 65.2 - while being 1.5x faster at inference time and leveraging a completely different information edge.
Perfectly Imperfect: A Speculation about Wabi-Sabi Inspired User Experience Design

Businesses and the software built for them are usually associated with models that have economic benefit at their core. The various disciplines of design, such as communication design for branding or design for human computer interaction (HCI), support those value-creating processes. Wabi-Sabi, a Japanese philosophy and aesthetic concept that is not rooted in creating monetary value, approaching a discipline within HCI, or vice versa, therefore asks for pathways that allow open, interdisciplinary and intercultural learning. We used speculative and narrative methods such as poetic writing and scriptwriting to prototype and create a narrative to illustrate our concepts. This paper investigates how Wabi-Sabi principles could be applied to the user experience of digital products, specifically business software. It further highlights a methodological approach that has proved valuable in investigating two initially contrary themes.
Identifying Key Factors Affecting on Future of Text Information Retrieval: a Cross-Impact Analysis Method

Identifying Key Factors Affecting on Future of Text Information Retrieval: a Cross-Impact Analysis Method
Optimizing Fully Spectral Convolutional Neural Networks on FPGA

Computing convolutional neural networks (CNNs) in frequency domain largely reduces the number of operations for training and inference of CNNs. However, existing designs with such an idea require repeated spatial- and frequency-domain switching due to the absence of nonlinear functions in the frequency domain, as such it makes the benefit less attractive for low-latency inference. This paper presents a fully spectral CNN approach by proposing a novel spectral Rectified Linear Unit (ReLU) activation function to avoid multiple compute-intensive domain transformations. Our proposal maintains the non-linearity in the network and takes into account the hardware efficiency in algorithm design. Then, a customized hardware architecture is proposed to implement the designed spectral network on FPGA device. Optimization techniques such as merging layers and DSP configuration are performed to further increase the inference speed. The hardware accelerator is implemented on Intel's Arria 10 device. Experimental results indicate that the proposed spectral-based design achieves 6.0× and 4.2× speedup compared to the spatial and FFT-based designs on the same device respectively, within only 0.5 percent accuracy loss on MNIST dataset.
Learning to Take Directions One Step at a Time

We present a method to generate a video sequence given a single image. Because items in an image can be animated in arbitrarily many different ways, we introduce as control signal a sequence of motion strokes. Such control signal can also be automatically transferred from other videos, e.g., via bounding box tracking. Each motion stroke provides the direction to the moving object in the input image and we aim to train a network to generate an animation following a sequence of such directions. To address this task we design a novel recurrent architecture, which can be trained easily and effectively thanks to an explicit separation of past, future and current states. As we demonstrate in the experiments, our proposed architecture is capable of generating an arbitrary number of frames from a single image and a sequence of motion strokes. Key components of our architecture are an autoencoding constraint to ensure consistency with the past and a generative adversarial scheme to ensure that images look realistic and are temporally smooth. We demonstrate the effectiveness of our approach on the MNIST, KTH, Human3.6M, Push and Weizmann datasets.
Agents and Robots for Reliable Engineered Autonomy:A Perspective from the Organisers of AREA 2020

Multi-agent systems, robotics and software engineering are large and active research areas with many applications in academia and industry. The First Workshop on Agents and Robots for reliable Engineered Autonomy (AREA), organised the first time in 2020, aims at encouraging cross-disciplinary collaborations and exchange of ideas among researchers working in these research areas. This paper presents a perspective of the organisers that aims at highlighting the latest research trends, future directions, challenges, and open problems. It also includes feedback from the discussions held during the AREA workshop. The goal of this perspective is to provide a high-level view of current research trends for researchers that aim at working in the intersection of these research areas.
Annotating Arabic Texts with Linked Data

The evolution of the traditional Web towards the semantic Web allows the machine to be a first-order citizen on the Web and increases discoverability of and accessibility to the unstructured data on the Web. This evolution enables the Linked Data technology to be used as background knowledge bases for unstructured data, notably the texts, available nowadays on the Web. For the Arabic language, the current situation is less brightness; the content of the Arabic language on the Web doesn't reflect the importance of this language. Given the fact that Arabic is one of the most important languages in the Web, and unfortunately it is under-resourced, so creating linguistic resources for it now is a necessity. Thus, we developed a linguistic approach for annotating Arabic textual corpus with Linked Data, especially DBpedia, which is Linked Open Data (LOD) extracted from Wikipedia. This approach uses natural language techniques to shedding light on Arabic text with Linked Open Data. The evaluation results of this approach are encouraging, despite the high complexity of our independent-domain knowledge base and the reduced resources in Arabic natural language processing.
Use of a new approach to automated break transposition cipher system

Abstract Particle swarm optimization (PSO) based cryptanalysis has acquired much attention because it has a fast convergence rate. This paper investigates the use of a new approach which is PSO depending on the clustering algorithm (CLPSO) to break the transposition cipher system in appropriate time. The dynamic of CLPSO is different from existing PSO in population size, topology and the ways to find the best solution. CLPSO start’s with using high population size (particles) and then applying preprocessing operation in order to reduce this population and then partition this population into several clusters based on using semi CLARANS algorithm and allow particles to share information in different clusters and give the particles ability to move from one cluster to another depending on the values of fitness function. Experimental results of the proposed CLPSO were very promising and the results proved that the CLPSO algorithm reduces the number of tries that needed to attack long key transposition cipher in almost real time using ciphertext-only attack. This new method allows recovering key length up to 35 with no more than 58.3 second as maximum consuming time. In this case study, different parameters such as: population sizes (100-5000), key size (10 - 35) and length of ciphertext (250 - 5000) were used.
Virtual Camera Layout Generation using a Reference Video

We propose a method that generates a virtual camera layout of a 3D animation scene by following the cinematic intention of a reference video. From a reference video, cinematic features such as the start frame, end frame, framing, camera movement, and the visual features of the subjects are extracted automatically. The extracted information is used to generate the virtual camera layout, which resembles the camera layout of the reference video. Our method handles stylized as well as human characters with body proportions different from those of humans. We demonstrate the effectiveness of our approach with various reference videos and 3D animation scenes. The user evaluation results show that the generated layouts are comparable to layouts created by the artist, allowing us to assert that our method can provide effective assistance to both novice and professional users when positioning a virtual camera.
Inverse-Sybil Attacks in Automated Contact Tracing

Automated contract tracing aims at supporting manual contact tracing during pandemics by alerting users of encounters with infected people. There are currently many proposals for protocols (like the "decentralized" DP-3T and PACT or the "centralized" ROBERT and DESIRE) to be run on mobile phones, where the basic idea is to regularly broadcast (using low energy Bluetooth) some values, and at the same time store (a function of) incoming messages broadcasted by users in their proximity. In the existing proposals one can trigger false positives on a massive scale by an "inverse-Sybil" attack, where a large number of devices (malicious users or hacked phones) pretend to be the same user, such that later, just a single person needs to be diagnosed (and allowed to upload) to trigger an alert for all users who were in proximity to any of this large group of devices.