Reviewerly/exHarmony huggingface dataset:
Combining Transformer Generators with Convolutional Discriminators

Transformer models have recently attracted much interest from computer vision researchers and have since been successfully employed for several problems traditionally addressed with convolutional neural networks. At the same time, image synthesis using generative adversarial networks (GANs) has drastically improved over the last few years. The recently proposed TransGAN is the first GAN using only transformer-based architectures and achieves competitive results when compared to convolutional GANs. However, since transformers are data-hungry architectures, TransGAN requires data augmentation, an auxiliary super-resolution task during training, and a masking prior to guide the self-attention mechanism. In this paper, we study the combination of a transformer-based generator and convolutional discriminator and successfully remove the need of the aforementioned required design choices. We evaluate our approach by conducting a benchmark of well-known CNN discriminators, ablate the size of the transformer-based generator, and show that combining both architectural elements into a hybrid model leads to better results. Furthermore, we investigate the frequency spectrum properties of generated images and observe that our model retains the benefits of an attention based generator.
New Hybrid Techniques for Business Recommender Systems

Besides the typical applications of recommender systems in B2C scenarios such as movie or shopping platforms, there is a rising interest in transforming the human-driven advice provided, e.g., in consultancy via the use of recommender systems. We explore the special characteristics of such knowledge-based B2B services and propose a process that allows incorporating recommender systems into them. We suggest and compare several recommender techniques that allow incorporating the necessary contextual knowledge (e.g., company demographics). These techniques are evaluated in isolation on a test set of business intelligence consultancy cases. We then identify the respective strengths of the different techniques and propose a new hybridisation strategy to combine these strengths. Our results show that the hybridisation leads to substantial performance improvement over the individual methods.
The Probabilistic Turn in Semantics and Pragmatics

This article provides an overview of graded and probabilistic approaches in semantics and pragmatics. These approaches share a common set of core research goals: ( a) a concern with phenomena that are best described as graded, including a vast lexicon of words whose meanings adapt flexibly to the contexts in which they are used, as well as reasoning under uncertainty about interlocutors, their goals, and their strategies; ( b) the need to show that representations are learnable, i.e., that a listener can learn semantic representations and pragmatic reasoning from data; ( c) an emphasis on empirical evaluation against experimental data or corpus data at scale; and ( d) scaling up to the full size of the lexicon. The methods used are sometimes explicitly probabilistic and sometimes not. Previously, there were assumed to be clear boundaries among probabilistic frameworks, classifiers in machine learning, and distributional approaches, but these boundaries have been blurred. Frameworks in semantics and pragmatics use all three of these, sometimes in combination, to address the four core research questions above.
Drowning Detection Based on Video Anomaly Detection

People are always trouble with drowning problems and want a drowning detection method. Almost existing methods extract simulated drowning features for supervised classification, but drowning events are rare abnormal events that are difficult to really simulate. In this paper, an unsupervised video anomaly detection method is proposed to detect pool drowning events. At first, we make a new dataset of pool scenes. Drowning events are only in the test set and the train set only includes pool normal events. Pool dataset is preprocessed and a neural network modified from ResNet is proposed to reconstruct input video frames. The differences between reconstructed frames and ground truth frames are compared to detect anomalous events not in the training set. Experiments show that proposed method is more applicable to video anomaly detection in pool scenes than existing methods and it is feasible that methods based on video anomaly detection for drowning detection.
A New Open-Source Off-Road Environment for Benchmark Generalization of Autonomous Driving

Recently, deep neural networks have greatly improved autonomous driving. However, as a great deal of training data is required, most studies have employed simulators. Generalization of such driving is key in terms of safety. The simulated environments feature only small variations in favorable conditions and thus cannot be used for benchmarking. Therefore, we developed a new open-source (OpenAI Gym-like) off-road environment featuring differently structured forests, plateaus, deserts, and snowfields. The dynamic topographical structures make the off-road environment a very challenging generalization problem. Our offroad environment can precisely evaluate autonomous driving in terms of generalization. Additionally, we proposed an evaluation method based on the success rate of driving tasks, enabling effective driving ability measurement. Furthermore, we evaluate the performance of existing end-to-end driving methods in our off-road environment. The results show that the end-to-end driving methods lack generalization ability and fail to generalize to unseen environments. Our off-road environment can help autonomous driving researchers develop a better, generalizable driving system. Unreal engine-level assets and codes are available at github.coma. We briefly introduce our model in https://www.youtube.com/watch?v=YIKd5bcHcGA.
Joint User Grouping, Version Selection, and Bandwidth Allocation for Live Video Multicasting

The key challenges in live video multicasting include how to properly form multicast groups, select video versions and allocate wireless resources, in order to guarantee the quality of experience (QoE) while ensuring low latency delivery. To address these challenges, in this paper, a novel multicast framework that leverages the advantages of network-assisted dynamic adaptive streaming over HTTP and cloud radio access networks is proposed, where a multicast assistant server is deployed at the edge of a mobile network. Under this architecture, a joint user grouping, version selection, and bandwidth allocation method is designed to optimize the sum of users’ utilities. In particular, a two-step scheme is proposed to solve this complex problem. The number of multicast groups is first automatically determined and a user clustering method is presented. Then, group-level version selection and spectrum assignment algorithms are performed at different time scales. Simulation results demonstrate that our proposed scheme can improve at least 7% QoE compared to baseline methods.
Root Cause Analyses for the Deteriorating Bitcoin Network Synchronization

The Bitcoin network synchronization is crucial for its security against partitioning attacks. From 2014 to 2018, the Bitcoin network size has increased, while the percentage of synchronized nodes has decreased due to block propagation delay, which increases with the network size. However, in the last few months, the network synchronization has deteriorated despite a constant network size. The change in the synchronization pattern suggests that the network size is not the only factor in place, necessitating a root cause analysis of network synchronization. In this paper, we perform a root cause analysis to study four factors that affect network synchronization: the unreachable nodes, the addressing protocol, the information relaying protocol, and the network churn. Our study reveals that the unreachable nodes size is 24x the reachable network size. We also found that the network addressing protocol does not distinguish between reachable and unreachable nodes, leading to inefficiencies due to attempts to connect with unreachable nodes/addresses. We note that the outcome of this behavior is a low success rate of the outgoing connections, which reduces the average outdegree. Through measurements, we found malicious nodes that exploit this opportunity to flood the network with unreachable addresses. We also discovered that Bitcoin follows a round-robin relaying mechanism that adds a small delay in block propagation. Finally, we observe a high churn in the Bitcoin network where ≈8 % nodes leave the network every day. In the last few months the churn among synchronized nodes has doubled, which is likely the most dominant factor in decreasing network synchronization. Consolidating our insights, we propose improvements in Bitcoin Core to increase network synchronization.
A Comparative Study on Sentiment Analysis Influencing Word Embedding Using SVM and KNN

Development of sentiment analysis is one of the most active research areas that relates natural language and social networks. In our proposed work, we have done sentiment analysis on an annotated list of positive and negative sentiment words from dataset opinion-lexicon-English. Here to perform our task, we used pretrained word embedding that converts words into numeric vectors and forms the basis for a classifier. Word2vec, a commonly used algorithm, includes CBOW and Skip-gram model in learning word embedding which is basically used for calculating the word vector. Finally, feature vectors are used to train SVM and KNN classifier. Here, we got testing accuracy of 96.2% for SVM classifier and 93.4% for KNN classifier.
K-means clustering algorithm and Python implementation

K-means is a commonly used algorithm in machine learning. It is an unsupervised learning algorithm. It is regularly used for data clustering. Only the number of clusters are needed to be specified for it to automatically aggregate the data into multiple categories, the similarity between data in the same cluster is high, thus, the similarity of data in different clusters is low. K-means algorithm is a typical distance-based clustering algorithm. It takes distance as the evaluation index of similarity, that is, the closer the distance between two objects, the greater similarity. Clustering is also extremely extensive in practical applications, such as: market segmentation, social network analysis, organized computing clusters, and astronomical data analysis. This paper is my own attempt to make K-means code and API, using Python and Java to jointly complete a project. The Python is mainly used to write the framework of the core algorithm of K-means, and the Java to create experimental data. In this research report, I will describe the simple data model provided by K-means, as well as the design and implementation of K-means.
Cloning One’s Voice Using Very Limited Data in the Wild

With the increasing popularity of speech synthesis products, the industry has put forward more requirements for personalized speech synthesis: (1) How to use low-resource, easily accessible data to clone a person’s voice. (2) How to clone a person’s voice while controlling the style and prosody. To solve the above two problems, we proposed the Hieratron model framework in which the prosody and timbre are modeled separately using two modules, therefore, the independent control of timbre and the other characteristics of audio can be achieved while generating speech. The practice shows that, for very limited target speaker data in the wild, Hieratron has obvious advantages over the traditional method, in addition to controlling the style and language of the generated speech, the mean opinion score on speech quality of the generated speech has also been improved by more than 0.2 points.
Improved quantum secret sharing based on entanglement swapping

Using entanglement swapping, a quantum secret sharing protocol is proposed. After sharing three-EPR among users, for encoding the message, the sender prepares an auxiliary EPR pair and by operating defined Pauli operations, she transmits six-bit message to her two agents. Since using entanglement swapping, no qubits carrying message are transmitted. So, if the perfect quantum channel is used, the protocol is unconditional secure. In addition, this protocol enhances the efficiency of Zhang-Man and Chou et al. protocols and with the comparison of previous works has better performance.
Secure Multi-Keyword Search and Access Control over Electronic Health Records in Wireless Body Area Networks

Wireless body area network (WBAN) consists of a number of sensors that are worn on patients to collect dynamic e-health records (EHRs) and mobile devices that aggregate EHRs. These EHRs are encrypted at mobile devices and then uploaded to the public cloud for storage and user access. To share encrypted EHRs with users effectively, help users retrieve EHRs accurately, and ensure EHRs confidentiality, a secure multi‐keyword search and access control (SMKS-AC) scheme is proposed, which implements encrypted EHRs access control under the ciphertext-policy attribute-based encryption (CP-ABE). SMKS-AC provides multi‐keyword search for accurate EHRs retrieval, supports the validation of decrypted EHRs, and traces and revokes the identity of users who leak private key. Security analysis shows that SMKS-AC is secure against chosen keyword and chosen plaintext attacks. Through theoretical analysis and experimental verification, the proposed SMKS-AC scheme requires less storage resources and computational costs on mobile devices than existing schemes.
Multidimensional Blockchain Security Analysis

Scaling and securing data exchange in distributed systems are important directions for scientific research. Known solutions provide certain level of security but lack some qualities required for wide usage. Among the obstacles are scaling distributed solutions and constructing exchange between separate robust distributed ledgers. The paper presents multidimensional blockchain— an approach to building robust distributed ledgers based on the notion of blockchain. The solution can be implemented in two approaches: block mode and state machine mode. A general security analysis, mathematical security analysis, and GUC-framework analysis are performed. Mathematical analysis is based on existing works on security of one-dimensional blockchain and consensus algorithms. A GUC-model is created to prove security of multidimensional blockchain. It is proven that multidimensional blockchain securely implements robust distributed ledger and provides secure inter-system exchange given that search and verification protocol is secure. The work is a basis for further research towards implementation of scalable robust distributed ledger.
Summary of the Third Nurse Care Activity Recognition Challenge - Can We Do from the Field Data?

research-article Share on Summary of the Third Nurse Care Activity Recognition Challenge - Can We Do from the Field Data? Authors: Sayeda Shamma Alia Kyushu Institute of Technology, Japan Kyushu Institute of Technology, JapanView Profile , Kohei Adachi Kyushu Institute of Technology, Japan Kyushu Institute of Technology, JapanView Profile , Tahera Hossain Kyushu Institute of Technology, Japan Kyushu Institute of Technology, JapanView Profile , Nhat Tan Le Kyushu Institute of Technology, Japan Kyushu Institute of Technology, JapanView Profile , Haru Kaneko Kyushu Institute of Technology, Japan Kyushu Institute of Technology, JapanView Profile , Paula Lago Kyushu Institute of Technology, Japan Kyushu Institute of Technology, JapanView Profile , Tsuyoshi Okita Kyushu Institute of Technology, Japan Kyushu Institute of Technology, JapanView Profile , Sozo Inoue Kyushu Institute of Technology, Japan Kyushu Institute of Technology, JapanView Profile Authors Info & Claims UbiComp '21: Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable ComputersSeptember 2021 Pages 428–433https://doi.org/10.1145/3460418.3479391Online:24 September 2021Publication History 2citation77DownloadsMetricsTotal Citations2Total Downloads77Last 12 Months77Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Development of a Low-Cost Data Acquisition System for Very Short-Term Photovoltaic Power Forecasting

The rising adoption of renewable energy sources means we must turn our eyes to limitations in traditional energy systems. Intermittency, if left unaddressed, may lead to several power-quality and energy-efficiency issues. The objective of this work is to develop a working tool to support photovoltaic energy forecast models for real-time operation applications. The current paradigm of intra-hour solar-power forecasting is to use image-based approaches to predict the state of cloud composition for short time horizons. Since the objective of intra-minute forecasting is to address high-frequency intermittency, data must provide information on and surrounding these events. For that purpose, acquisition by exception was chosen as the guiding principle. The system performs power measurements at 1 Hz frequency, and whenever it detects variations over a certain threshold, it saves the data 10 s before and 4 s after the detection point. A multilayer perceptron neural network was used to determine its relevance to the forecasting problem. With a thorough selection of attributes and network structures, the results show very low error with R2 greater than 0.93 for both input variables tested with a time horizon of 60 s. In conclusion, the data provided by the acquisition system yielded relevant information for forecasts up to 60 s ahead.
Fly Out the Window: Exploiting Discrete-Time Flatness for Fast Vision-Based Multirotor Flight

Recent work has demonstrated fast, agile flight using only vision as a position sensor and no GPS. Current feedback controllers for fast vision-based flight typically rely on a full-state estimate, including position, velocity and acceleration. An accurate full-state estimate is often challenging to obtain due to noisy IMU measurements, infrequent position updates from the vision system, and an imperfect motion model used to obtain high-rate state estimates required by the controller. In this letter, we present an alternative control design that bypasses the need for a full-state estimate by exploiting discrete-time flatness, a structural property of the underlying vehicle dynamics. First, we show that the Euler discretization of the multirotor dynamics is discrete-time flat. This allows us to design a predictive controller using only a window of inputs and outputs, the latter consisting of position and yaw estimates. We highlight in simulation that our approach outperforms controllers that rely on an incorrect full-state estimate. We perform extensive outdoor multirotor flight experiments and demonstrate reliable vision-based navigation. In these experiments, our discrete-time flatness-based controller achieves speeds up to 10 m/s and significantly outperforms similar controllers that hinge on full-state estimation, achieving up to 80% path error reduction.
Improved MIMO Signal Detection Based on DNN in MIMO-OFDM System

This paper proposes the multiple-input multiple-output (MIMO) detection scheme by using the deep neural network (DNN) based ensemble machine learning for higher error performance in wireless communication systems.For the MIMO detection based on the ensemble machine learning, all learning models for the DNN are generated in offline and the detection is performed in online by using already learned models.In the offline learning, the received signals and channel coefficients are set to input data, and the labels which correspond to transmit symbols are set to output data.In the online learning, the perfectly learned models are used for signal detection where the models have fixed bias and weights.For performance improvement, the proposed scheme uses the majority vote and the maximum probability as the methods of the model combinations for obtaining diversity gains at the MIMO receiver.The simulation results show that the proposed scheme has improved symbol error rate (SER) performance without additional receive antennas.
Prediction of Eating Activity using Smartwatch

Eating activity understanding has been extensively studied for its importance in our lives. Due to the lack of proper dietary management, obesity has been increasing worldwide, which causes many diseases. In this regard, adequate diet management is crucial for having a healthier life. This study aims to develop a system that allows users to manage their diet easily. This research proposed a method that estimates eating activities. The main objective of this research is to use an eating manner (way of eating) to identify more accurately the eating activity by arm's acceleration using a smartwatch. The acceleration data were collected using a smartwatch during different mealtime. A total of 12 features are extracted from this data. We have examined different classifiers' performance. Among them, the Support Vector Machine (cubic SVM) classifier performed best among all. An evaluation of the system resulted in eating activities detected with the maximum accuracy of 76.4% and an average accuracy of 71.9% using 10-folds cross-validation.
Twitter based sentiment analysis to predict public emotions using machine learning algorithms

Twitter is a prominent social media platform where users may send and receive messages known as “tweets.” Individuals can use this to communicate their opinions or opinions regarding a variety of topics. Sentiment analysis has been performed on such tweets by a variety of parties, including consumers and advertisers, in order to get insights about goods or conduct market research. Additionally, recent advances in machine learning techniques have enhanced the exactitude of sentiment analysis forecasts. In this work, sentiment analysis on “tweets” was performed utilizing a variety of machine learning approaches. It attempts to grade the tweet's polarity as either positive or negative. If a tweet good and negative rudiments, the overall mood ought to be used to classify it. In this research work, Kaggle dataset was used and that had been crawled and categorized as positive or negative. Emoticons, usernames, and hash tags are included in the data, which must be processed and transformed into a standard format. The suggested research project must also extract relevant aspects from the text, just as unigrams and bigrams, that are two different ways to express a “tweet.” Ensembling is a type of meta learning algorithm methodology in which researchers mix many classifiers to increase prediction accuracy. Finally, the study shows that Deep Learning approaches outperform other methods.
Prevention and mitigation measures against phishing emails: a sequential schema model

Phishing emails have permeated our digital communication, taking advantage of vulnerabilities that the information technology system poses to users. Given the potential for further cybersecurity incidents, theft of personally identifiable information, and damage to organizations’ assets, cybersecurity professionals have implemented various mitigation practices to combat phishing emails. This paper categorizes current mitigation practices in relation to a sequential schema adopted from the situational crime prevention approach, so as to enable a more organized and strategic assessment of human and environmental vulnerabilities. Our model could be useful for cybersecurity professionals to further advance mitigation measures as an incident progresses and for criminologists and other academic researchers to reduce the severity of subsequent criminal incidents.