Reviewerly/exHarmony huggingface dataset:
Domain-Specific Versus General-Purpose Word Representations in Sentiment Analysis for Deep Learning Models

Sentiment analysis, also known as opinion mining or emotion artificial intelligence, has been gradually becoming a hot-trend topic in the recent years due to its wide applications and also the challenges in handling the dataset efficiently. With the popular deep learning-based approach, using vectors of word as an input for the models is a good way for the machine to learn the relation between words in a document. In this paper, vector representations of word are created based on two different ways: learning from its own internal dataset, or deriving from a famous pre-trained library known as fastText. The two representations will then be used in documents (we classify sentiment on document level) to generate input to 4 deep learning models: Long-Short Term Memory Network (LSTM), Bi-directional Long-Short Term Memory Network (BiLSTM), Convolutional Neural Network (CNN) and Convolutional Neural Network combined with Long-Short Term Memory Network (CNN-LSTM). We do experiment in Vietnamese review dataset and explorer words in domain-specific and general–purpose before turning into vectors in two different ways: tokenized and not tokenized. The result shows the best result of each of the approaches in four deep learning models: LSTM, Bi-LSTM, CNN and CNN-LSTM.
Repetition Detection in a Dynamic String

A string UU for a non-empty string U is called a square. Squares have been well-studied both from a combinatorial and an algorithmic perspective. In this paper, we are the first to consider the problem of maintaining a representation of the squares in a dynamic string S of length at most n. We present an algorithm that updates this representation in n^o(1) time. This representation allows us to report a longest square-substring of S in O(1) time and all square-substrings of S in O(output) time. We achieve this by introducing a novel tool - maintaining prefix-suffix matches of two dynamic strings. We extend the above result to address the problem of maintaining a representation of all runs (maximal repetitions) of the string. Runs are known to capture the periodic structure of a string, and, as an application, we show that our representation of runs allows us to efficiently answer periodicity queries for substrings of a dynamic string. These queries have proven useful in static pattern matching problems and our techniques have the potential of offering solutions to these problems in a dynamic text setting.
Off-Resonance Control and All-Optical Switching: Expanded Dimensions in Nonlinear Optics

The theory of non-resonant optical processes with intrinsic optical nonlinearity, such as harmonic generation, has been widely understood since the advent of the laser. In general, such effects involve multiphoton interactions that change the population of each input optical mode or modes. However, nonlinear effects can also arise through the input of an off-resonant laser beam that itself emerges unchanged. Many such effects have been largely overlooked. Using a quantum electrodynamical framework, this review provides detail on such optically nonlinear mechanisms that allow for a controlled increase or decrease in the intensity of linear absorption and fluorescence and in the efficiency of resonance energy transfer. The rate modifications responsible for these effects were achieved by the simultaneous application of an off-resonant beam with a moderate intensity, acting in a sense as an optical catalyst, conferring a new dimension of optical nonlinearity upon photoactive materials. It is shown that, in certain configurations, these mechanisms provide the basis for all-optical switching, i.e., the control of light-by-light, including an optical transistor scheme. The conclusion outlines other recently proposed all-optical switching systems.
False Image Injection Prevention Using iChain

The advances in information and communication technology are consistently beneficial for the healthcare sector. A trend in the healthcare sector is the progressive shift in how data are acquired and the storage of such data in different facilities, such as in the cloud, due to the efficiency and effectiveness offered. Digital images related to healthcare are sensitive in nature and require maximum security and privacy. A malicious entity can tamper with such stored digital images to mislead healthcare personnel and the consequences of wrong diagnosis are harmful for both parties. A new type of cyber attack, a false image injection attack (FIIA) is introduced in this paper. Existing image tampering detection measures are unable to guarantee tamper-proof medical data in real time. Inspired by the effectiveness of emerging blockchain technology, a security framework, image chain (iChain) is proposed in this paper to ensure the security and privacy of the sensitive healthcare images. The practical challenges associated with the proposed framework and further research that is required are also highlighted.
Evaluating the Brazilian Portuguese version of the 2015 LIWC Lexicon with sentiment analysis in social networks

LIWC is a text analysis program that categorizes words into gram- matical and psychologically derived categories. The currently available LIWC lexicon for Brazilian Portuguese (LIWC 2007pt) is based on the 2007 version of LIWC program. As several studies indicated, LIWC 2007pt shows perfor- mance and categorization problems. In this scenario, this work highlights a new Brazilian Portuguese LIWC lexicon (LIWC 2015pt), based on LIWC 2015 program. This work compares the performance of LIWC 2007pt and LIWC 2015pt in classification tasks. Three experiments were conducted and the results indi- cate LIWC 2015pt outperforms LIWC 2007pt in all three tasks.
High-resolution grain size distribution of sediment core with hyperspectral imaging

The study of sediment cores allows for the reconstruction of past climate and environment through physical-chemical analysis. Nevertheless, this interpretation suffers from many drawbacks that can be overcome with the newest technologies. Hyperspectral imaging is one of these and allows a fast, high resolution, and non-destructive analysis of sediment cores. In this study, we use visible and near-infrared hyperspectral imaging to predict particle size fractions and distribution (PSD) at a resolution of 200 μm on a previously well-studied sediment core taken from Lake Bourget (Western Alps, France). These predictions agree with previous studies on this core. Then, the PSD was used to estimate sedimentary deposit sources using the PSD unmixing algorithm AnalySize. It permitted estimation of the contribution of five sources (micrite, small and large bio-induced calcite crystals, diatom frustules, detrital particles), which had previously been characterized. The spatial dimension allowed for laminae to be discretized and counted, in agreement with the age-depth model previously established. We then evaluated the particle size and spectral signatures of each of these annual laminae, hence characterizing their physico-chemical composition. These high-resolution data also allowed for estimation of the accumulation rate (cm/year) of each of the main sources in the laminated unit and inferring the trophic status and the presence of instantaneous events of the lake.
Optimized Renewable Energy Use in Green Cloud Data Centers

The huge energy consumption of cloud data centers not only increases costs but also carbon emissions associated with such data centers. Powering data centers with renewable or green sources of energy can reduce brown energy use and consequently carbon emissions. However, powering data centers with these energy sources is challenging, as they are variable and not available at all times. In this work, we formulate the microservices management problem as finite Markov Decision Processes (MDP) to optimise renewable energy use. By dynamically switching off non-mandatory microservices and scheduling battery usage, upon the user’s preference, our proposed method makes a trade-off between the workload execution and brown energy consumption. We evaluate our proposed method using traces derived from two real workloads and real-world solar data. Simulated experiments show that, compared with baseline algorithms, our proposed approach performs up to 30% more efficiently in balancing the brown energy usage and workload execution.
Beyond a House of Sticks

Current efforts establishing semantic metadata standards for the built environment span academia [3], industry [1] and standards bodies [2, 28]. For these standards to be effective, they must be clearly defined and easily extensible, encourage consistency in their usage, and integrate cleanly with existing industrial standards, such as BACnet. There is a natural tension between informal tag-based systems that rely upon idiom and convention for meaning, and formal ontologies amenable to automated tooling.
Development of ceiling board using breadfruit seed coat and recycled low density polyethylene

Conversion of agro wastes into potential raw materials for production has gained a lot of attention in research. This has led to the development of ceiling board using waste breadfruit seed coat and Recycled Low Density Polyethylene, as filler and binder respectively. The filler material (Breadfruit seed coat), was treated with 1 mol/dm3 of NaOH to eliminate the pigment and neutralized using 0.5 mol/dm3 Acetic Acid. After drying, the filler material was ground to a particle size of 600μm. Using Central Composite Design (CCD) tool of the Design Expert software, the experimental design was set up. From the design, thirty experimental samples were developed with the production parameters; press time, press pressure, press temperature and filler/rLDPE ratio as the independent variables. The produced samples were tested for Thermal Conductivity, Water Absorption, Thickness Swell and Density tests, which formed the responses for the Central Composite Design. The results of the experiment were analysed using the Response Surface Methodology while the validation was done using the Analysis of Variance (ANOVA). The optimal values obtained for the production parameters are 19.722% filer/rLDPE, 10minutes press time, 197.31 °C press temperature and 9.042MPa press pressure, which gave a 775.661 g/cm3 density, 0.308% Water Absorption, 0.962% Thickness Swell and 0.367W/M.K Thermal Conductivity. This result shows that agro waste breadfruit seed coat is a good filler material for the production of ceiling board.
Leveraging Medical Literature for Section Prediction in Electronic Health Records

Electronic Health Records (EHRs) contain both structured content and unstructured (text) content about a patient's medical history.In the unstructured text parts, there are common sections such as Assessment and Plan, Social History, and Medications.These sections help physicians find information easily and can be used by an information retrieval system to return specific information sought by a user.However, it is common that the exact format of sections in a particular EHR does not adhere to known patterns.Therefore, being able to predict sections and headers in EHRs automatically is beneficial to physicians.Prior approaches in EHR section prediction have only used text data from EHRs and have required significant manual annotation.We propose using sections from medical literature (e.g., textbooks, journals, web content) that contain content similar to that found in EHR sections.Our approach uses data from a different kind of source where labels are provided without the need of a timeconsuming annotation effort.We use this data to train two models: an RNN and a BERTbased model.We apply the learned models along with source data via transfer learning to predict sections in EHRs.Our results show that medical literature can provide helpful supervision signal for this classification task.Plan Medications: HUMULIN INJ 70/30 20 u ac breakfast PRINIVIL TABS 20 MG 1 qd Treatment: Will have annual foot exam at next visit. . . .
Ontological engineering for supporting semantic reasoning in design: deriving models based on ontologies for supporting engineering design

The aim of this section is to provide an overview on the state of the current research, the exposition of the challenges faced in various fields of knowledge formalisation and representation (KFR),...
Optimal Coherent Filtering for Single Noisy Photons

We introduce a filter using a noise-free quantum buffer with large optical bandwidth that can both filter temporal-spectral modes as well as interconvert them and change their frequency. We theoretically show that such quantum buffers optimally filter out temporal-spectral noise, producing identical single photons from many distinguishable noisy single-photon sources with the minimum required reduction in brightness. We then experimentally demonstrate a noise-free quantum buffer in a warm atomic system that is well matched to quantum dots. Based on these experiments, simulations show that our buffer can outperform all intensity (incoherent) filtering schemes for increasing indistinguishability.
Learning Heuristics for Quantified Boolean Formulas through Reinforcement Learning

We demonstrate how to learn efficient heuristics for automated reasoning algorithms for quantified Boolean formulas through deep reinforcement learning. We focus on a backtracking search algorithm, which can already solve formulas of impressive size - up to hundreds of thousands of variables. The main challenge is to find a representation of these formulas that lends itself to making predictions in a scalable way. For a family of challenging problems, we learned a heuristic that solves significantly more formulas compared to the existing handwritten heuristics.
Medication Regimen Extraction from Medical Conversations

Extracting relevant information from medical conversations and providing it to doctors and patients might help in addressing doctor burnout and patient forgetfulness. In this paper, we focus on extracting the Medication Regimen (dosage and frequency for medications) discussed in a medical conversation. We frame the problem as a Question Answering (QA) task and perform comparative analysis over: a QA approach, a new combined QA and Information Extraction approach, and other baselines. We use a small corpus of 6,692 annotated doctor-patient conversations for the task. Clinical conversation corpora are costly to create, difficult to handle (because of data privacy concerns), and thus scarce. We address this data scarcity challenge through data augmentation methods, using publicly available embeddings and pretrain part of the network on a related task (summarization) to improve the model’s performance. Compared to the baseline, our best-performing models improve the dosage and frequency extractions’ ROUGE-1 F1 scores from 54.28 and 37.13 to 89.57 and 45.94, respectively. Using our best-performing model, we present the first fully automated system that can extract Medication Regimen tags from spontaneous doctor-patient conversations with about \(\sim \)71% accuracy.
Identifying and Implementing Relevant Research Data Management Services for the Library at the University of Dodoma, Tanzania

<p class="Pa6">Research Data Management (RDM) services are increasingly becoming a subject of interest for academic and research libraries globally – this is also the case in developing countries. The interest is motivated by a need to support research activities through data sharing and collaboration both locally and internationally. Many institutions, especially in the developed countries, have implemented RDM services to accelerate research and innovation through e-Research but extensive RDM is not so common in developing countries. In reality many African universities and research institutions are yet to implement the most basic of data management services. We believe that the absence of political will and national government mandates on data management often hold back the development and implementation of RDM services. Similarly, research funding agencies are not yet applying sufficient pressure to ensure that Africa complies with the requirement to deposit research data in trusted repositories. While the context was acknowledged the University of Dodoma library staff realized that it is urgent to prepare for the inevitable – the time when RDM will be a requirement for research funding support. <p class="Pa6">This paper presents the results of research conducted at the University of Dodoma, Tanzania. The purpose of the research was to identify and report on relevant RDM services that need to be implemented so that researchers and university management could collaborate and make our research data accessible to the international community. <p class="Pa6">This paper presents findings on important issues for consideration when planning to develop and implement RDM services at a developing country academic institution. The paper also mentions the requirements for the sustainability of these initiatives.
Design and Implementation of a Hybrid Anomaly Detection System for IoT

In recent years, the dramatic increase in the number of devices has empowered the Internet of Things (IoT). Unfortunately though, IoT networks are susceptible to cyberattacks, due to the limited capabilities of the nodes. Since conventional security designs do not consider such limitations, the development of new solutions, suitable for IoT networks has become an urgent task. In this paper, we propose a modular hybrid anomaly detection system (ADS) for IoT. The proposed system utilizes cloud computing to detect anomalies in both application and network layers and train a neural network in a centralized manner. The obtained neural network weights are then downloaded to the IoT devices. This architecture allows the IoT devices to detect anomalies in a local manner, thereby reducing the communication overhead and detection latency. Also, the ADS has a mechanism to measure the deviation between the local models and the central model. Then, the deviation is used to set the frequency at which the model updates. This allows the system to update the local models less frequently when the deviation is low, further reducing the overhead. The ADS was deployed on a test IoT system and the results proved the advantages of the proposed mechanism in decreasing the detection latency and the communications overhead, while improving the detection accuracy locally.
A Phishing Webpage Detection Method Based on Stacked Autoencoder and Correlation Coefficients

Phishing is a kind of cyber-attack that targets naive online users by tricking them into revealing sensitive information. There are many anti-phishing solutions proposed to date, such as blacklist or whitelist, heuristic-based and machine learning-based methods. However, online users are still being trapped into revealing sensitive information in phishing websites. In this paper, we propose a novel phishing webpage detection model, based on features that are extracted from URL, source codes of HTML, and the third-party services to represent the basic characters of phishing webpages, which uses a deep learning method – Stacked Autoencoder (SAE) to detect phishing webpages. To make features in the same order of magnitude, three kinds of normalization methods are adopted. In particular, a method to calculate correlation coefficients between weight matrixes of SAE is proposed to determine optimal width of hidden layers, which shows high computational efficiency and feasibility. Based on the testing of a set of phishing and benign webpages, the model using SAE achieves the best performance when compared to other algorithms such as Naive Bayes (NB), Support Vector Machine (SVM), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). It indicates that the proposed detection model is promising and can be applied effectively to phishing detection.
On Succinct Groundings of HTN Planning Problems

Both search-based and translation-based planning systems usually operate on grounded representations of the problem. Planning models, however, are commonly defined using lifted description languages. Thus, planning systems usually generate a grounded representation of the lifted model as a preprocessing step. For HTN planning models, only one method to ground lifted models has been published so far. In this paper we present a new approach for grounding HTN planning problems that produces smaller groundings in a shorter timespan than the previously published method.
Unsupervised classification of snowflake images using a generative adversarial network and &amp;lt;i&amp;gt;K&amp;lt;/i&amp;gt;-medoids classification

Abstract. The increasing availability of sensors imaging cloud and precipitation particles, like the Multi-Angle Snowflake Camera (MASC), has resulted in datasets comprising millions of images of falling snowflakes. Automated classification is required for effective analysis of such large datasets. While supervised classification methods have been developed for this purpose in recent years, their ability to generalize is limited by the representativeness of their labeled training datasets, which are affected by the subjective judgment of the expert and require significant manual effort to derive. An alternative is unsupervised classification, which seeks to divide a dataset into distinct classes without expert-provided labels. In this paper, we introduce an unsupervised classification scheme based on a generative adversarial network (GAN) that learns to extract the key features from the snowflake images. Each image is then associated with a distribution of points in the feature space, and these distributions are used as the basis of K-medoids classification and hierarchical clustering. We found that the classification scheme is able to separate the dataset into distinct classes, each characterized by a particular size, shape and texture of the snowflake image, providing signatures of the microphysical properties of the snowflakes. This finding is supported by a comparison of the results to an existing supervised scheme. Although training the GAN is computationally intensive, the classification process proceeds directly from images to classes with minimal human intervention and therefore can be repeated for other MASC datasets with minor manual effort. As the algorithm is not specific to snowflakes, we also expect this approach to be relevant to other applications.
Gender Recognition by Monitoring Walking Patterns via Smartwatch Sensors

Activity-based computing aims to monitor the physical state of a user by sending continuous signals through sensors attached to the subject's body. This research field is becoming very important with the increasing number of smart devices. The sensors embedded in these smart devices can be used to monitor user's activities. There has been significant work recognizing human activities. But in this paper, we propose a system to recognize the gender of the subject by monitoring the walking pattern. To capture the walking pattern of the subject, a smart watch was attached to the subject's wrist, while accelerometer and gyroscope signals was recorded at a fixed interval. After preprocessing the raw data, an artificial neural network was used to classify the gender of the subject. We reviewed the data mining technique to capture sensor signals, implemented an artificial neural network for classification purposes, analyzed the major challenges and introduced many real-life applications.