Reviewerly/exHarmony huggingface dataset:
Energy Anomaly Detection and Modelling on Smart Premises using SDAR

Energy as a commodity is facing a globally prevalent shortage with the ever increasing gap between the demand and supply. The strain on the non-renewable conventional energy resources is evident from the multitude of industries in the manufacturing hubs of China that have come to standstill in the face of non-availability of coal. The phenomenon is particularly acute in all developing countries with an unbalanced approach to the energy management vis-a-vis distribution losses. The Internet of Things (IoT) ecosystem enabled smart homes and industrial premises have shown promising application towards achieving energy efficiency via creation of dynamically adjusting demand based systems. It is possible to predict the energy consumption requirements based on application of Machine Learning (ML) based models on statistical data obtained from energy sensors. The data streams from IoT devices however, more often than not throw up surprises in the form of outliers and changes which can affect the Machine learning based time series forecasting. The problem is more accentuated in the case of non-stationary time series sources where it is imperative to ascertain whether an anomaly is momentarily affecting the time series as an outlier or it is a permanent change never returning to the original trend. This paper uses a sequentially discounting auto regression (SDAR) learning algorithm to detect and classify the anomalies in energy consumption usage for model accuracy. Specifically, we have applied the online SDAR algorithm on energy consumption IoT dataset from kaggle as a demonstration to distinguish between outliers and permanent changes over the time series which can be used for interpretability and increasing model accuracy while prediction of energy consumption. We were able to forecast sudden changes in the energy consumption requirements well in advance, based on the previous years&#x0027; usage patterns as the results indicate.
Enhancing Face Super-Resolution via Improving the Edge and Identity Preserving Network

Face super-resolution, known as face hallucination, is a domain-specific image super-resolution problem, which refers to generating high resolution face images from their low resolution. State-of-the-art face super-resolution methods used deep convolutional neural networks. However, due to significant pose changes and difficulty in recovering high-frequency details in facial areas, most of these methods do not deploy facial structures and identity information well, and it is tough for them to reconstruct super-resolved face images. According to previous researches, proper use of low-resolution image edges can be a solution for these problems. EIPNet (Edge and Identity Preserving Network) is one of the newest methods to achieve outstanding results in this area. In the EIPNet method, the authors used a lightweight edge extraction block in the proposed GAN structure. In this research, we intend to improve the performance of the EIPNet method by presenting a simple but efficient technique. Our proposed technique divides the face images into upper and lower parts. We train a separate network for each area. This technique reduces the number of face components to train from each area, and the networks can better be trained from their components. The results show that this technique can have an excellent effect on visual quality and quantitative measurements in face super-resolution.
ERGAN: High Perform GAN for Eyeglasses Removal

To solve the occlusion of eyeglasses on the eye area, which brings great challenges to face recognition and gaze tracking. Based on the successful application of face attribute editing task in Generative Adversarial Network, this paper proposes a new eyeglasses removal method ERGAN. The latent space of GANs has rich semantic information. By manipulating latent codes, different attributes of images can be changed. The key is to find the appropriate semantic direction. First of all, in order to enable GAN to process real images, we introduce the more advanced GAN inversion model IDInvert, which can convert the given real image back to the latent space of the pre-trained GAN model. Then use our model ERGAN to remove eyeglasses attributes. Subsequently, to find the appropriate semantic direction of eyeglasses editing, we add the semantic direction of the eyeglasses instance to the semantic direction calculated by InterfaceGAN. Recombine the two semantic directions into the semantic direction of eyeglasses editing. Finally, experiments are performed on GANs generated images and real images. The experimental results show that our method is helpful to improve the accuracy of eyeglasses removal.
Sources and destinations of misattributions in recall of instances of repeated events

Repeated experience of events promotes schema formation. Later activation of the schema facilitates recall of the general structure of the events, whereas attribution of details to instances requires systematic decision-making based on detail characteristics. For repeated events, source monitoring may be less effective due to the similarity and interference of details across instances and consequently result in source attribution errors. To date, researchers have examined aggregated misattributions across instances and have found that misattributions are more frequent in the middle than in the boundary instances. In this study, we investigated the trajectories of misattributions using data from six studies (N = 633), where participants recalled repeated interactive marketing-themed events (Study 1), mock-crime filmed events (Study 2), stories (Study 3), and categorized word lists (Studies 4–6). The patterns confirmed the expected primacy and recency effects, showing fewer misattributions from and to the boundary instances relative to the middle instances. In addition, the patterns indicated proximity effects: Confusions more frequently occurred across adjacent instances and gradually decreased for instances that were further apart from the source. Our findings suggest that detail characteristics that form the basis of source attribution decisions provide information about the relative position of instances in repeated events, where the boundary instances serve as anchors, and where confusion relatively easily occurs across neighbouring instances. In line with context-based models of memory, our findings indicate that a higher-level organization of repeated events that emerges at encoding guides retrieval and source monitoring decisions.
FPGA-Based High-Performance Data Compression Deep Neural Network Accelerator

Deep neural networks play an important role in extracting valuable information from massive amounts of data. But these networks require huge computational and memory overhead, which hinders their use in resource-limited environments, such as mobile or embedded devices. In order to solve this problem, researchers usually reduce the amount of data and the number of memory accesses to reduce the overhead caused by data transmission. In this paper, we design a compressed storage and calculation fusion (CSCF) algorithm for massive input data to compress the input data volume and improve the processing efficiency of terminal equipment. Firstly, we scan and compress the collected data, then classify and store the compressed data according to the location of consecutive zero-valued pixel blocks. In order to adapt to actual development scenarios, we choose FPGA hardware architecture with high flexibility, low energy consumption, and short development cycle as the terminal processor. Therefore, we design a classification calculation unit corresponding to classification compression and storage on the FPGA architecture, and improve the performance of the model by fusing the first-layer convolution calculation of the convolution neural network and the compression storage of the input data. The evaluation results show that, compared with the traditional neural network accelerator for uncompressed transmission, our CSCF-FPGA accelerator achieves a speedup of 3.8-4.8 times on the MNIST data set and 1.8-2.1 times on the CIFAR series data set. Small fluctuations in speedup ratio and hardware resource utilization show that CSCF-FPGA not only achieves good performance, but also brings no additional hardware loss.
Open-Ended Search for Environments and Adapted Agents Using MAP-Elites

Creatures in the real world constantly encounter new and diverse challenges they have never seen before. They will often need to adapt to some of these tasks and solve them in order to survive. This almost endless world of novel challenges is not as common in virtual environments, where artificially evolving agents often have a limited set of tasks to solve. An exception to this is the field of open-endedness where the goal is to create unbounded exploration of interesting artefacts. We want to move one step closer to creating simulated environments similar to the diverse real world, where agents can both find solvable tasks, and adapt to them. Through the use of MAP-Elites we create a structured repertoire, a map, of terrains and virtual creatures that locomote through them. By using novelty as a dimension in the grid, the map can continuously develop to encourage exploration of new environments. The agents must adapt to the environments found, but can also search for environments within each cell of the grid to find the one that best fits their set of skills. Our approach combines the structure of MAP-Elites, which can allow the virtual creatures to use adjacent cells as stepping stones to solve increasingly difficult environments, with open-ended innovation. This leads to a search that is unbounded, but still has a clear structure. We find that while handcrafted bounded dimensions for the map lead to quicker exploration of a large set of environments, both the bounded and unbounded approach manage to solve a diverse set of terrains.
Cycle-GAN based Face Aging Method

In this study, we try to explore the possibility of cycle-GAN in the field of face aging and the effect of cycle-GAN based aging model. The reason why we used cycle-GAN for this study is that cycle-GAN does not require paired data as a training set. Paired data is expensive and difficult to obtain paired data. Therefore, if unpaired data can be used as a training set, it will greatly reduce the cost of facial aging. We plan to use PSNR and SSIM to measure the truthfulness of cycle-GAN generated images. We then quantified the aging effect using a CNN-based DEX network to assess the age of the aging images. In the process of research, we found that the model was unbalanced in processing images of different genders and ethnic groups, so we tried to make the model more balanced in the above cases through classification training and pre-training, which achieved good results. Finally, we developed a GUI as a convenient interface for users to use cycle- GAN model to do face aging. Through a series of quantitative analyses, we were able to conclude that our model has a good effect in the field of facial aging and is superior to other aging models based on deep learning. We also provide the code of this project on GitHub: https://github.com/MeditatorE/Face-time-travel-machine
A Framework Based on Personality Traits to Identify Vulnerabilities to Social Engineering Attacks

Currently, all users massively use digital devices connected to the Internet. Because of this overcrowding, the number of cybercriminals who use Social Engineering techniques to extract sensitive information from users also increased. The attackers take advantage of the personality traits of their victims, such as friendliness, ignorance of basic security measures, naivety, or overconfidence. To combat this type of attack, we have developed a Framework based on personality traits, allowing us to know which people are more vulnerable than others are. Faced with this scenario, the main aim of this study is to develop a tool that allows determining which people are most vulnerable to Social Engineering attacks. The methodological process consisted of first determining the most common traits of users related to vulnerability. Then, the personality traits of a surveyed group were determined. Finally, the most vulnerable traits were matched with the personality traits of the respondents. The personality traits in which our research was framed is known as the Five-Factor Model of personality. The results show that this framework can be used to detect the most vulnerable user or groups and focus training and awareness on it or these groups.
Knowledge-Enhanced Retrieval: A Scheme for Question Answering

Chinese Knowledge Base Question Answering (CKBQA), as a significant task in natural language processing, has drawn massive attention from both industry and academia. However, previous studies mainly concentrated on multi-hop questions, which may limit the performance of tackling complex natural questions with various forms. To that end, in this paper, we propose a comprehensive technical framework called Knowledge-Enhanced Retrieval Question Answering (KERQA) for tackling complex questions, which could precisely extract the gold answers to these questions from a large-scale knowledge graph. Specifically, our proposed KERQA follows the pipeline with five modules, including the Question Classification module to categorize questions, the Named Entity Recognition module to extract mentions, and the Entity Linking module to match entities in the knowledge graph (KG). Along this line, we further design the Path Generation module to associate the paths in the KG with predefined templates, as well as the Path Ranking module to capture the best path. Extensive validations demonstrate the effectiveness of our KERQA framework, which achieved an F1 score of 78.78% on the final leaderboard of the CCKS 2021 KBQA contest.
Real Time Action Recognition from Video Footage

Crime rate is increasing proportionally with the increasing rate of the population. The most prominent approach was to introduce Closed-Circuit Television (CCTV) camera-based surveillance to tackle the issue. Video surveillance cameras have added a new dimension to detect crime. Several research works on autonomous security camera surveillance are currently ongoing, where the fundamental goal is to discover violent activity from video feeds. From the technical viewpoint, this is a challenging problem because analyzing a set of frames, i.e., videos in temporal dimension to detect violence might need careful machine learning model training to reduce false results. This research focuses on this problem by integrating state-of-the-art Deep Learning methods to ensure a robust pipeline for autonomous surveillance for detecting violent activities, e.g., kicking, punching, and slapping. Initially, we designed a dataset of this specific interest, which contains 600 videos (200 for each action). Later, we have utilized existing pre-trained model architectures to extract features, and later used deep learning network for classification. Also, We have classified our models’ accuracy, and confusion matrix on different pre-trained architectures like VGG16, InceptionV3, ResNet50, Xception and MobileNet V2 among which VGG16 and MobileNet V2 performed better.
Domain Adaptation as Optimal Transport on Grassmann Manifolds

Domain adaptation in the Euclidean space is a challenging task on which researchers recently have made great progress. However, in practice, there are rich data representations that are not Euclidean. For example, many high-dimensional data in computer vision are in general modeled by a low-dimensional manifold. This prompts the demand of exploring domain adaptation between non-Euclidean manifold spaces. This article is concerned with domain adaption over the classic Grassmann manifolds. An optimal transport-based domain adaptation model on Grassmann manifolds has been proposed. The model implements the adaption between datasets by minimizing the Wasserstein distances between the projected source data and the target data on Grassmann manifolds. Four regularization terms are introduced to keep task-related consistency in the adaptation process. Furthermore, to reduce the computational cost, a simplified model preserving the necessary adaption property and its efficient algorithm is proposed and tested. The experiments on several publicly available datasets prove the proposed model outperforms several relevant baseline domain adaptation methods.
An Enhanced Approach to Predict Re-occurrences of Breast Cancer Using Machine Learning

The most affected cancer among women is breast cancer (BC). After skin cancer, the most affected cancer in the world is BC. The term re-occurrence is used for BC, which affects after successful treatment. The prediction of re-occurrence BC can reduce the chances of death. In this paper, we proposed a machine learning approach and neural network model to calculate recurrence chances of BC. We used the Wisconsin dataset of the UCI machine learning repository in our approach, implemented an artificial neural network (ANN), and compared the results with the support vector machine. Our model outperformed when compared to other models with an accuracy of 95% with support vector machine.
On Optimizing Transaction Fees in Bitcoin using AI: Investigation on Miners Inclusion Pattern

The transaction-rate bottleneck built into popular proof-of-work (PoW)-based cryptocurrencies, like Bitcoin and Ethereum, leads to fee markets where transactions are included according to a first-price auction for block space. Many attempts have been made to adjust and predict the fee volatility, but even well-formed transactions sometimes experience unexpected delays and evictions unless a substantial fee is offered. In this article, we propose a novel transaction inclusion model that describes the mechanisms and patterns governing miners decisions to include individual transactions in the Bitcoin system. Using this model we devise a Machine Learning (ML) approach to predict transaction inclusion. We evaluate our predictions method using historical observations of the Bitcoin network from a five month period that includes more than 30 million transactions and 120 million entries. We find that our Machine Learning (ML) model can predict fee volatility with an accuracy of up to 91%. Our findings enable Bitcoin users to improve their fee expenses and the approval time for their transactions.
The Impact of the New Mediators of the Digital Age on Freedom of Speech

New technologies have had an impact that is both positive and negative on freedom of speech, constitutional rights and democratic processes. It was positive in the early stages of the development of the Internet and particularly in the early stages of Web 2.0, when the Internet was designed in a more participative and cooperative manner. In recent years, nevertheless, hierarchical processes of information and data organisation have appeared through large technological companies that monopolise the distribution of information and opinion and which are the new mediators between users and the public sphere. Freedom of speech is currently constrained by these mediators, namely large technological companies that control the communicative processes. This paper analyses the role that these new mediators are developing, taking into account their impact on freedom of speech and on the configuration of the public sphere in democratic systems. Two elements stand out among the new mediators: the dialectic of freedom of speech is shifting from the public to the private sphere and from the state to the global sphere. These are two elements that together help fuel the power of the new mediators and weaken the state’s capacity for regulation and control. But in the ecosystems developed by technology companies, the new mediators exercise a power that is not strictly private since they occupy and monopolise a public sphere. In the environment created by the new mediators, freedom of expression becomes a mere commercial product, so that information and opinion are transformed into ephemeral merchandise organised through the algorithms of Internet applications, which decide their impact on and their incidence in the public sphere. These algorithms have been created with an economic purpose and promote fake news and radicalisation to attract the attention of the public and thus generate greater profit. The new mediators, by promoting fake news in democratic contexts (without trying to impose a specific narrative, as in dictatorial ones), generate a destructive tension about reality. Instead of contributing like the traditional media does to the social construction of reality or as in dictatorships to the reconstruction of reality based on the interests of the dominant oligarchy, they are causing the “destruction” of reality, that is, of a shared social perception of reality. Among the many measures that can be adopted, those related to competition law stand out, with institutional measures through regulators that may avert an even greater concentration of power. However, rather than restrictions, it is openness that is desirable—open technology that puts an end to the closed-off, hierarchical nature of applications. Telephone communication, for example, is open and allows mobile phone operators to operate, making global communication possible, and the same is true for e-mail servers. Communication applications that are currently closed-off (WhatsApp and Telegram, for example) should also be open, intercommunicable and managed by a plurality of operators.
Performance Analysis of Docker Containerization and Virtualization

Data centers are the beating heart of Internet and cloud. Virtualization is one of the major technologies in data centers. Cloud computing is slower as compared with the on-premises or native environments. But its advantages weigh much more. Virtualization technologies have seen constant rise in recent years with cloud is booming the IT industry like never before. Applications are deployed using containers and virtual machines with faster and secure deployment environments. In the current industry, we are seeing containers and virtual machines as two major technologies to run application while reducing the wastage of computing resources. Performance and security of applications running in these environments are compared in our experimental work with parameters like CPU performance, file I/O, and memory utilization are compared among both the virtualization technologies. Apart from above parameters, we have also used HTTP load from different continents that is used to showcase how the application running on container and virtual machine perform with traffic around the world.
Robust Risley prism control based on disturbance observer for system line-of-sight stabilization

In this paper, a robust control based on disturbance observer is proposed to improve the tracking accuracy of the Risley prism system (RPS). Applying the flexible thin-wall ring mechanism in the RPS causes a series of tracking and pointing challenges. Disturbances such as friction, shaft deformation, and model perturbation significantly deteriorate the tracking and pointing accuracy of the RPS. Two different observer-based control methods are proposed to guarantee the tracking precision of the RPS. Moreover, the disturbance observation and compensation (DOC) performance of the proposed methods is analyzed and compared. Finally, simulation and experiment results indicate that the proposed control methods, especially the DOC-expanded state observer control mode, obtain the best performance for disturbance rejection in the RPS.