Reviewerly/exHarmony huggingface dataset:
PSRMER: Proactive Services Recommendation Driven-by Multimodal Emotion Recognition

In the past three years, global COVID-19 pandemic not only impacted people’s physical health but also significantly affected their mental health, which resulting in rapid increase of psychological problems. Emotions are a common manifestation of psychological changes, and some services (such as music, video, or psychological counseling services) can help users to adjust their emotions in a timely manner, thus to avoid bringing extreme events (e.g., running away from home or committing suicide). Therefore, how to perceive users’ real-time emotions and then recommend the most appropriate services to users has become a challenge. To address this issue, this work proposes an approach for proactive services recommendation driven-by multimodal emotion recognition (named as PSRMER). Specifically, PSRMER first actively identifies a user’s emotion with a multimodal emotion recognition model based on BiGRU and Transformer; Then, considering the user’s emotion and preferences, PSRMER selects the optimal services based on an index-graph linking different emotions and various services; Finally, PSRMER proactively recommends the selected optimal service to the user. Extensive experiments have been conducted and the effectiveness of our proposed method have been proved. Moreover, the proposed method can also be used in smart education, smart transportation, smart elderly care and other modern industry fields.
Emotion Recognition Model using Product Review from Indonesia Marketplace

Online marketplace platforms create opportunities for vendors to sell their products to consumers online. During the early phase of the COVID pandemic, the adoption of online shopping in Indonesia is rapidly increasing. This rising state of online marketplace usage produces abundant data and allows researchers to conduct studies and explore. Product review data in the online marketplace contain rich information for emotion analysis tasks. This research proposes an emotion recognition model using the PRDECT-ID dataset. The TF-IDF approach is utilized as the baseline model for this research. Then, the authors focus on fine-tuning IndoBERT to outperform the baseline model. For the evaluation, the F1-Score was used to evaluate the models’ performance. This research found that the best configuration model is fine-tuned IndoBERT-base-p1 with 3e-6 as the learning rate, 32 for the batch size, and training with four epochs. The F1-Score of fine-tuned IndoBERT is 71%, surpassing the TF-IDF model by 9%.
A Feature Distribution Smoothing Network Based on Gaussian Distribution for QoS Prediction

With the increasing number of services and their homogenization, the use of Quality of Service (QoS) for recommendations has become necessary. However, existing QoS prediction solutions have limitations in solving the noise and label imbalance problems of dataset, which greatly limit the improvement of QoS prediction accuracy. In this paper, we propose FSNet that contains a feature distribution smoothing module and an improved W-Huber loss function. The feature distribution smoothing module mitigates the effect of noise problem by fitting potential Gaussian distribution of known features with a supervised feedforward neural network. W-Huber loss function mitigates the impact of label imbalance problem on QoS prediction by reweighting the two components of Huber loss function. We conduct extensive experiments on real large-scale QoS dataset, and the results demonstrate that the proposed FSNet method outperforms existing QoS prediction methods.
Precision-aware Latency and Energy Balancing on Multi-Accelerator Platforms for DNN Inference

The need to execute Deep Neural Networks (DNNs) at low latency and low power at the edge has spurred the development of new heterogeneous Systems-on-Chips (SoCs) encapsulating a diverse set of hardware accelerators. How to optimally map a DNN onto such multi-accelerator systems is an open problem. We propose ODiMO, a hardware-aware tool that performs a fine-grain mapping across different accelerators on-chip, splitting individual layers and executing them in parallel, to reduce inference energy consumption or latency, while taking into account each accelerator's quantization precision to maintain accuracy. Pareto-optimal networks in the accuracy vs. energy or latency space are pursued for three popular dataset/DNN pairs, and deployed on the DIANA heterogeneous ultra-low power edge AI SoC. We show that ODiMO reduces energy/latency by up to 33%/31% with limited accuracy drop (−0.53%/-0.32%) compared to manual heuristic mappings.
A Probabilistic Public-Key Encryption with Ensuring Data Integrity in Cloud Computing

Researchers are working hard to face various challenges in the fast-moving field of network security and data exchange. When it comes to encryption techniques, efforts vary to address multiple issues such as computational speed, data size requirements, application scope, etc. that are driven by the purposes of collecting and sharing sensitive data that must be stored and processed securely. This paper proposes an efficient asymmetric cryptosystem that bases on the multiplication of plaintext by a variable public key. The origin of this key is constant, but for each encryption, a random number must be added to it. Therefore, a new public key each time is generated; in other words, a simulation of a one-time pad system. For each message, the sender generates a new secret twin of the public key; The receiver can decrypt all messages encrypted by different twins with a single private key. The proposed probabilistic scheme is linear coding and hence is lightweight, easy to execute, and practical in many areas. We consider the cloud to be an untrusted part that tries to disclose data when decoded by systems, the proposed method provides the additive homomorphic property and ensures data integrity when a homomorphic operation is performed by the cloud; thus, we realize verifiable partial homomorphic encryption.
Classifying blinking and winking EOG signals using statistical analysis and LSTM algorithm

Abstract Detection of eye movement types whether the movement of the eye itself or blinking has attracted a lot of recent research. In this paper, one method to detect the type of wink or blink produced by the eye is scrutinized and another method is proposed. We discuss what statistical analysis can teach us about detection of eye movement and propose a method based on long short-term memory (LSTM) networks to detect those types. The statistical analysis is composed of two main steps, namely calculation of the first derivative followed by a digitization step. According to the values of the digitized curve and the duration of the signal, the type of the signal is detected. The success rate reached 86.6% in detection of the movement of the eye when those volunteers are not trained on using our system. However, when they are trained, the detection success rate reached 93.3%. The statistical analysis succeeds in achieving detection of all types of eye movement except one type which is the non-intentional blinking. Although rate of success achieved is high, but as the number of people using this system increases, the error in detection increases that is because it is fixed and not adaptive to changes. However; we learnt from statistical analysis that the first derivative is a very important feature to classify the type of an EOG signal. Next, we propose using the LSTM network to classify EOG signals. The effect of using the first derivative as a feature for identifying the type of EOG signals is discussed. The LSTM algorithm succeeds in detecting the type of EOG signals with a percentage equal to 92% for all types of eye movement.
Insulator Abnormal Condition Detection from Small Data Samples

Insulators are an important part of transmission lines in active distribution networks, and their performance has an impact on the power system's normal operation, security, and dependability. Traditional insulator detection methods, on the other hand, necessitate a significant amount of labor and material resources, necessitating the development of a new detection method to substitute manpower. This paper investigates the abnormal condition detection of insulators based on UAV vision sensors using artificial intelligence algorithms from small samples. Firstly, artificial intelligence for the image data volume requirements was large, i.e., the insulator image samples taken by the UAV vision sensor inspection were not enough, or there was a missing image problem, so the data enhancement method was used to expand the small sample data. Then, the YOLOV5 algorithm was used to compare detection results before and after the extended dataset's optimization to demonstrate the expanded dataset's dependability and universality, and the results revealed that the expanded dataset improved detection accuracy and precision. The insulator abnormal condition detection method based on small sample image data acquired by the visual sensors studied in this paper has certain theoretical guiding significance and engineering application prospects for the safe operation of active distribution networks.
Application of Artificial Intelligence Algorithms in Multilayer Perceptron and Elman Networks to Predict Photovoltaic Power Plant Generation

This paper presents the models developed for the short-term forecasting of energy production by photovoltaic panels. An analysis of a set of weather factors influencing daily energy production is presented. Determining the correlation between the produced direct current (DC) energy and the individual weather parameters allowed the selection of the potentially best explanatory factors, which served as input data for the neural networks. The forecasting models were based on MLP and Elman-type networks. An appropriate selection of structures and learning parameters was carried out, as well as the process of learning the models. The models were built based on different time periods: year-round, semi-annual, and seasonal. The models were developed separately for monocrystalline and amorphous photovoltaic modules. The study compared the models with the predicted and measured insolation energy. In addition, complex forecasting models were developed for the photovoltaic system, which could forecast DC and AC energy simultaneously. The complex models were developed according to the rules of global and local modeling. The forecast errors of the developed models were included. The smallest values of the DC energy forecast errors were achieved for the models designed for summer forecasts. The percentage forecast error was 1.95% using directly measured solar irradiance and 5. 57% using predicted solar irradiance. The complex model for summer forecasted the AC energy with an error of 1.86%.
Can local explanation techniques explain linear additive models?

Abstract Local model-agnostic additive explanation techniques decompose the predicted output of a black-box model into additive feature importance scores. Questions have been raised about the accuracy of the produced local additive explanations. We investigate this by studying whether some of the most popular explanation techniques can accurately explain the decisions of linear additive models. We show that even though the explanations generated by these techniques are linear additives, they can fail to provide accurate explanations when explaining linear additive models. In the experiments, we measure the accuracy of additive explanations, as produced by, e.g., LIME and SHAP, along with the non-additive explanations of Local Permutation Importance (LPI) when explaining Linear and Logistic Regression and Gaussian naive Bayes models over 40 tabular datasets. We also investigate the degree to which different factors, such as the number of numerical or categorical or correlated features, the predictive performance of the black-box model, explanation sample size, similarity metric, and the pre-processing technique used on the dataset can directly affect the accuracy of local explanations.
Hidden non– <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math> -locality in linear networks

We study hidden nonlocality in a linear network with independent sources. In the usual paradigm of Bell nonlocality, there are certain states which exhibit nonlocality only after the application of suitable local filtering operations, which, in turn, are some special stochastic local operations assisted with classical communication (SLOCC). In the present work, we introduce the notion of hidden non $n$-locality. The notion is detailed using a bilocal network. We provide instances of hidden nonbilocality and nontrilocality, where we notice quite intriguingly that nonbilocality is observed even when one of the sources distributes a mixed two-qubit separable state. Furthermore, a characterization of hidden nonbilocality is also provided in terms of the Bloch-Fano decomposition, wherein we conjecture that, to witness hidden nonbilocality, one of the two states (used by the sources) must have nonnull local Bloch vectors. Noise is inevitable in practical scenarios, which makes it imperative to study any possible method to enhance the possibility of detecting nonclassicality in the presence of noise in the network. We find that local filtering enhances the robustness to noise, which we demonstrate using bit-flip and amplitude-damping channels.
Content moderation through removal of service: Content delivery networks and extremist websites

Abstract Considerable attention has been paid by researchers to social media platforms, especially the ‘big companies’, and increasingly also messaging applications, and how effectively they moderate extremist and terrorist content on their services. Much less attention has yet been paid to if and how infrastructure and service providers, further down ‘the tech stack’, deal with extremism and terrorism. Content Delivery Networks (CDN) such as Cloudflare play an underestimated role in moderating the presence of extremist and terrorist content online as it is impossible for these websites to operate without DDoS protection. This is evidenced by the takedown of a wide range websites such as The Daily Stormer, 8chan, a variety of Taliban websites and more recently the organised harassment site Kiwifarms following refusal of service by Cloudflare. However, it is unclear whether there is any formal process of content review conducted by the company when it decides to refuse services. This article aims to first provide an analysis of what extremist and terrorist websites make use of Cloudflare's services as well as other CDNs, and how many of them have been subject to takedown following refusal of service. Following this the article analyses CDNs' terms of service and how current and upcoming internet regulation applies to these CDNs.
Distantly Supervised Relation Extraction via Contextual Information Interaction and Relation Embeddings

Distantly supervised relation extraction (DSRE) utilizes an external knowledge base to automatically label a corpus, which inevitably leads to the problem of mislabeling. Existing approaches utilize BERT to provide instances and relation embeddings to capture a wide set of relations and address the noise problem. However, the method suffers from a single method of textual information processing, underutilizing the feature information of entity pairs in the relation embeddings part and being interfered with by noisy labels when classifying multiple labels. For this reason, we propose the contextual information interaction and relation embeddings (CIRE) method. First, we utilize BERT and Bi-LSTM to construct a neural network model to enhance contextual information interaction by filtering and supplementing sequence information through the error repair capability of the Bi-LSTM gating mechanism. At the same time, we combine the vector difference between entity pairs and entity pairs in the relation embeddings layer to improve the relation embeddings accuracy. Finally, we choose sparse softmax as the classifier, which improves the ability to control the noise categories by controlling the number of output categories. The experimental results show that our method significantly outperforms the baseline method and improves the AUC metric by 2.6% on the NYT2010 dataset.
PVONet: point-voxel-based semi-supervision monocular three-dimensional object detection using LiDAR camera systems

Light detection and ranging (LiDAR) camera systems are becoming increasingly vital for autonomous driving. The monocular three-dimensional (3D) detection task is a critical and challenging aspect of this field. However, most algorithms rely solely on manually labeled images, which is a time-consuming and labor-intensive process, and the resulting detection lacks depth information. To address this problem, a semi-supervised 3D object detection model based on LiDAR camera systems (PVONet) is proposed to improve both the detection accuracy and processing time. First, an innovative data preparation block point-voxel fusion estimation is introduced; it utilizes LiDAR points to generate 3D bounding boxes for unlabeled data, thereby significantly reducing the time compared with manual labeling. Second, a new block based on fully connected neural network for box estimation (feature extraction and 3D object detection) is presented; it is utilized to conduct feature extraction, feature correlation, and 3D box estimation on monocular images. Finally, comprehensive experiments conducted on the popular KITTI 3D detection dataset demonstrate that our PVONet is faster (30 ms on KITTI benchmark) and more accurate [with increases of 4.69%/3.82% (easy), 4.45%/2.79% (moderate), and 4.07%/3.75% (hard) aggregation processes on 3D/bird's eye view objects compared with the baseline]. This meets the requirements for high real-time performance in autonomous vehicles applications. The results demonstrate the effectiveness of our model based on LiDAR camera systems.
PBA-YOLOv7: An Object Detection Method Based on an Improved YOLOv7 Network

Deep learning-based object detection methods address the problem of how to trade off the object detection accuracy and detection speed of the model. This paper proposes the PBA-YOLOv7 network algorithm, which is based on the YOLOv7 network, and first introduces the PConv, which lightens the ELAN module in the backbone network structure and reduces the number of parameters to improve the detection speed of the network and then designs and introduces the BiFusionNet network, which better aggregates the high-level semantic features and the low-level semantic features; and finally, on this basis, the coordinate attention mechanism is introduced to make the network focus on more critical features without increasing the model complexity. The coordinate attention mechanism is introduced to make the network focus more on important feature information and improve the feature expression ability of the network without increasing the model complexity. Experiments on the publicly available KITTI’s dataset show that the PBA-YOLOv7 network model significantly improves both detection accuracy and detection speed compared to the original YOLOv7 model, with 4% and 7.8% improvement in mAP0.5 and mAP0.5:0.95, respectively, and six frames improvement in FPS. The improved algorithm in this paper weighs the model’s detection accuracy and detection speed in the detection task. It performs well compared to other algorithms, such as YOLOv7 and YOLOv5l.
MDAU-Net: A Liver and Liver Tumor Segmentation Method Combining an Attention Mechanism and Multi-Scale Features

In recent years, U-Net and its extended variants have made remarkable progress in the realm of liver and liver tumor segmentation. However, the limitations of single-path convolutional operations have hindered the full exploitation of valuable features and restricted their mobility within networks. Moreover, the semantic gap between shallow and deep features proves that a simplistic shortcut is not enough. To address these issues and realize automatic liver and tumor area segmentation in CT images, we introduced the multi-scale feature fusion with dense connections and an attention mechanism segmentation method (MDAU-Net). This network leverages the multi-head attention (MHA) mechanism and multi-scale feature fusion. First, we introduced a double-flow linear pooling enhancement unit to optimize the fusion of deep and shallow features while mitigating the semantic gap between them. Subsequently, we proposed a cascaded adaptive feature extraction unit, combining attention mechanisms with a series of dense connections to capture valuable information and encourage feature reuse. Additionally, we designed a cross-level information interaction mechanism utilizing bidirectional residual connections to address the issue of forgetting a priori knowledge during training. Finally, we assessed MDAU-Net’s performance on the LiTS and SLiver07 datasets. The experimental results demonstrated that MDAU-Net is well-suited for liver and tumor segmentation tasks, outperforming existing widely used methods in terms of robustness and accuracy.
3D Reconstruction of Fishes Using Coded Structured Light

3D reconstruction of fishes provides the capability of extracting geometric measurements, which are valuable in the field of Aquaculture. In this paper, a novel method for 3D reconstruction of fishes using the Coded Structured Light technique is presented. In this framework, a binary image, called pattern, consisting of white geometric shapes, namely symbols, on a black background is projected onto the surface of a number of fishes, which belong to different species. A camera captures the resulting images, and the various symbols in these images are decoded to uniquely identify them on the pattern. For this purpose, a number of steps, such as the binarization of the images captured by the camera, symbol classification, and the correction of misclassifications, are realized. The proposed methodology for 3D reconstructions is adapted to the specific geometric and morphological characteristics of the considered fishes with fusiform body shape, something which is implemented for the first time. Using the centroids of the symbols as feature points, the symbol correspondences immediately result in point correspondences between the pattern and the images captured by the camera. These pairs of corresponding points are exploited for the final 3D reconstructions of the fishes. The extracted 3D reconstructions provide all the geometric information which is related to the real fishes. The experimentation demonstrates the high efficiency of the techniques adopted in each step of the proposed methodology. As a result, the final 3D reconstructions provide sufficiently accurate approximations of the real fishes.