Reviewerly/exHarmony huggingface dataset:
Graph Deep Factors for Probabilistic Time-series Forecasting

Effective time-series forecasting methods are of significant importance to solve a broad spectrum of research problems. Deep probabilistic forecasting techniques have recently been proposed for modeling large collections of time-series. However, these techniques explicitly assume either complete independence (local model) or complete dependence (global model) between time-series in the collection. This corresponds to the two extreme cases where every time-series is disconnected from every other time-series in the collection or likewise, that every time-series is related to every other time-series resulting in a completely connected graph. In this work, we propose a deep hybrid probabilistic graph-based forecasting framework called Graph Deep Factors (GraphDF) that goes beyond these two extremes by allowing nodes and their time-series to be connected to others in an arbitrary fashion. GraphDF is a hybrid forecasting framework that consists of a relational global and relational local model. In particular, a relational global model learns complex non-linear time-series patterns globally using the structure of the graph to improve both forecasting accuracy and computational efficiency. Similarly, instead of modeling every time-series independently, a relational local model not only considers its individual time-series but also the time-series of nodes that are connected in the graph. The experiments demonstrate the effectiveness of the proposed deep hybrid graph-based forecasting model compared to the state-of-the-art methods in terms of its forecasting accuracy, runtime, and scalability. Our case study reveals that GraphDF can successfully generate cloud usage forecasts and opportunistically schedule workloads to increase cloud cluster utilization by 47.5% on average. Furthermore, we target addressing the common nature of many time-series forecasting applications where time-series are provided in a streaming version; however, most methods fail to leverage the newly incoming time-series values and result in worse performance over time. In this article, we propose an online incremental learning framework for probabilistic forecasting. The framework is theoretically proven to have lower time and space complexity. The framework can be universally applied to many other machine learning-based methods.
Love Unrequited: The Story of Architecture, Agile, and How Architecture Decision Records Brought Them Together

Software architecture has long sought the attention of agile developers, but its love is unrequited. For decades, architects have offered up gifts to agile teams. In response, agile developers have responded with little more than a raised eyebrow. As a result, teams that blend architecture principles and agile practices are rare.
Design of sampling-based noniterative algorithms for centroid type-reduction of general type-2 fuzzy logic systems

Abstract General type-2 (GT2) fuzzy logic systems (FLSs) become a popular research topic for the past few years. Usually the Karnik–Mendel algorithms are the most prevalent approach to complete the type-reduction. Nonetheless, the iterative quality of these types of computational intensive algorithms might impede applying them. For the improved types of algorithms, some noniterative algorithms can enhance the calculation efficiencies greatly, while it is still an open problem for comparing the relation between the discrete TR algorithms and corresponding continuous TR algorithms. First, the sum and integral operations in discrete and continuous noniterative algorithms are compared. Then, three kinds of noniterative algorithms originate from the type-reduction of interval type-2 FLSs are extended to complete the centroid type-reduction of general T2 FLSs. Four computer simulations prove that while changing the number of samples suitably, the calculational results of discrete types of algorithms may accurately gain on the related continuous types of algorithms, and the calculational times of discrete types of algorithms are obviously less than the continuous types of algorithms, this may offer the possible meaning for designing and applying T2 FLSs.
Revisiting Frequency Analysis against Encrypted Deduplication via Statistical Distribution

Encrypted deduplication addresses both security and storage efficiency in large-scale storage systems: it ensures that each plaintext is encrypted to a ciphertext by a symmetric key derived from the content of the plaintext, so as to allow deduplication on the ciphertexts derived from duplicate plaintexts. However, the deterministic nature of encrypted deduplication leaks the frequencies of plaintexts, thereby allowing adversaries to launch frequency analysis against encrypted deduplication and infer the ciphertext-plaintext pairs in storage. In this paper, we revisit the security vulnerability of encrypted deduplication due to frequency analysis, and show that encrypted deduplication can be even more vulnerable to the sophisticated frequency analysis attack that exploits the underlying storage workload characteristics. We propose the distribution-based attack, which builds on a statistical approach to model the relative frequency distributions of plaintexts and ciphertexts, and improves the inference precision (i.e., have high confidence on the correctness of inferred ciphertext-plaintext pairs) of the previous attack. We evaluate the new attack against real-world storage workloads and provide insights into its actual damage.
Deep Learning Models for Emotion Classification in Human Robot Interaction Platforms

Human Robot Interaction (HRI) main purpose is to improve the communication between robots and people, in special the service robots which principal function is interacting with users. Service robots could be virtual or physical, such as a chatbot or humanoid robot. The increase of internet access and the use of online services have produced an exponentially use of chatbots. This situation generate people spending more time using this technology and trying to humanize it. Therefore, giving robots emotional capabilities have become an important issue in the field. For this reason, the purpose of this article is to analyzed and compared the performance of common deep learning techniques (CNN, RNN) that could be used as a emotion classifier for HRI platforms such a chatbots or humanoid robots. Two kind of input signals were evaluated: text and images of faces. In addition, different metrics were selected to evaluate the accuracy and time performance of the models.
Malicious Transaction Identification in Digital Currency via Federated Graph Deep Learning

With the rapid development of digital currencies in recent years, their anonymity provides a natural shelter for criminals. This problem resulting in various types of malicious transactions emerge in an endless stream, which seriously endangers the financial order of digital currencies. Many researchers have started to focus on this area and have proposed heuristics and feature-based centralized machine learning algorithms to discover and identify malicious transactions. However, these approaches ignore the existence of financial flows between digital currency transactions and do not use the important neighborhood relationships and rich transaction characteristics. In addition, centralized learning exposes a large amount of transaction feature data to the risk of leakage, where criminals may trace the actual users using traceability techniques. To address these issues, we proposes a graph neural network model based on federated learning named GraphSniffer to identify malicious transactions in the digital currency market. GraphSniffer leverages federated learning and graph neural networks to model graph-structured Bitcoin transaction data distributed at different worker nodes, and transmits the gradients of the local model to the server node for aggregation to update the parameters of the global model. GraphSniffer can realize the joint identification and analysis of malicious transactions while protecting the security of transaction feature data and the privacy of the model. Extensive experiments validate the superiority of the proposed method over the stateof-the-art.
Streaming Overlay Architecture for Lightweight LSTM Computation on FPGA SoCs

Long-Short Term Memory (LSTM) networks, and Recurrent Neural Networks (RNNs) in general, have demonstrated their suitability in many time series data applications, especially in Natural Language Processing (NLP) . Computationally, LSTMs introduce dependencies on previous outputs in each layer that complicate their computation and the design of custom computing architectures, compared to traditional feed-forward networks. Most neural network acceleration work has focused on optimising the core matrix-vector operations on highly capable FPGAs in server environments. Research that considers the embedded domain has often been unsuitable for streaming inference, relying heavily on batch processing to achieve high throughput. Moreover, many existing accelerator architectures have not focused on fully exploiting the underlying FPGA architecture, resulting in designs that achieve lower operating frequencies than the theoretical maximum. This paper presents a flexible overlay architecture for LSTMs on FPGA SoCs that is built around a streaming dataflow arrangement, uses DSP block capabilities directly, and is tailored to keep parameters within the architecture while moving input data serially to mitigate external memory access overheads. The architecture is designed as an overlay that can be configured to implement alternative models or update model parameters at runtime. It achieves higher operating frequency and demonstrates higher performance than other lightweight LSTM accelerators, as demonstrated in an FPGA SoC implementation.
A Comprehensive Survey: Cybersecurity Challenges and Futures of Autonomous Drones

Unmanned Aircraft Systems (UAS) also known as drones are changing the world [1]. Whether it’s military, medicine, agriculture, city planning, surveillance, package delivery or sports. These machines are useful in ways that haven’t been discovered yet. Making dangerous work safe by putting something between people and potential risks. Increasing the amount of land that can be searched in search and rescues. Drones are making difficult jobs easier. Conversely the danger they pose is also being discovered. Everyday a new danger is presented by the growing number of drones being used, and as we rely more on them the problem grows. This research paper looks into the current security issues that are found in drones and what is being done to lessen the risk that drones pose to countries, businesses, and civilians. Looking to find what works best to protect drones from cyber threats, and how to protect against them. This is done by reading research journals, news articles, watching videos done by professors/news organizations then analyzing the data found. Two frames of mind are used when analyzing the problems found from research. First, will be how to protect the drone from cyber threats. Second will be how to protect from drone cyber attacks. The results of this research found that drones are extremely unsafe against cyber attacks, and are powerful weapons to finding and exploiting holes in a systems defense. Research on defending drones found insufficient authentication, encryption, signal verification, defined access privileges, and threat reporting. These problems exist from hobby pilots to [2]Department of Defense (DOD) drones. The greatest tool a drone has when attacking a system is stealth and mobility. It can attack multiple targets with only a few fly bys. Because of this the best defense against drone cyber threats is detection. Once detected the drone could be dealt with physically or by exploiting security holes commonly found in drones. Resulting from the cybersecurity threat posed both to drones, and from drones [3, 4, 5, 6] laws are being written in multiple countries. These laws are designed to limit their usage or ban them completely. To solve this problem, there are three key steps to eliminating a majority of the threats. [7] Implementing authentication, encryption, and a threat reporting system would allow drones to continue to change the world safely.
Policy-Induced Unsupervised Feature Selection: A Networking Case Study

A promising approach for leveraging the flexibility and mitigating the complexity of future telecom systems is the use of machine learning (ML) models that can analyze the network performance, as well as taking proactive actions. A key enabler for ML models is timely access to reliable data, in terms of features, which require pervasive measurement points throughout the network. However, excessive monitoring is associated with network overhead. Considering domain knowledge may provide clues to find a balance between overhead reduction and meeting requirements on future ML use cases by monitoring just enough features. In this work, we propose a method of unsupervised feature selection that provides a structured approach in incorporation of the domain knowledge in terms of policies. Policies are provided to the method in form of must-have features defined as the features that need to be monitored at all times. We name such family of unsupervised feature selection the policy-induced unsupervised feature selection as the policies inform selection of the latent features. We evaluate the performance of the method on two rich sets of data traces collected from a data center and a 5G-mmWave testbed. Our empirical evaluations point at the effectiveness of the solution.
DRL-based Optimization of Privacy Protection and Computation Performance in MEC Computation Offloading

The emergence of mobile edge computing (MEC) imposes an unprecedented pressure on privacy protection, although it helps the improvement of computation performance including energy consumption and computation delay by computation offloading. To this end, we propose a deep reinforcement learning (DRL)-based computation offloading scheme to optimize jointly privacy protection and computation performance. The privacy exposure risk caused by offloading history is investigated, and an analysis metric is defined to evaluate the privacy level. To find the optimal offloading strategy, an algorithm combining actor-critic, off-policy, and maximum entropy is proposed to accelerate the learning rate. Simulation results show that the proposed scheme has better performance compared with other benchmarks.
A joint model and data driven track segment association algorithm for manoeuvring target tracking

In the field of manoeuvring target tracking, track breakage is a common issue due to strong manoeuvrability, missed detection, large measurement error, and long sampling interval, etc. In order to improve the track continuity of manoeuvring targets, a joint model and data-driven track segment association (JMDD-TSA) algorithm is proposed. First, the interacting multiple model (IMM) smoother is invoked to pre-process the track segments to obtain a smoothed state sequence. And the mode switching time (MST) is estimated by the IMM when a motion mode changing occurs in a track segment. Second, the estimated MST is used to select the partial smoothed states as the training data and the local Gaussian process (LGP) is proposed to predict and backtrack the track segments. Finally, a two-step association strategy is designed to assign and mend the candidate track segment pairs. Compared with the model-driven TSA and the data-driven TSA, the proposed algorithm can effectively improve the average track life and tracking accuracy of strong manoeuvring targets.
How to Optimize Student Learning Using Student Models That Adapt Rapidly to Individual Differences

An important component of many Adaptive Instructional Systems (AIS) is a ‘Learner Model’ intended to track student learning and predict future performance. Predictions from learner models are frequently used in combination with mastery criterion decision rules to make pedagogical decisions. Important aspects of learner models, such as learning rate and item difficulty, can be estimated from prior data. A critical function of AIS is to have students practice new content once the AIS predicts that they have ‘mastered’ current content or learned it to some criterion. For making this prediction, individual student parameters (e.g., for learning rate) are frequently unavailable due to having no prior data about a student, and thus population-level parameters or rules-of-thumb are typically applied instead. In this paper, we will argue and demonstrate via simulation and data analysis that even in best-case scenarios, learner models assuming equal learning rates for students will inevitably lead to systematic errors that result in suboptimal pedagogical decisions for most learners. This finding leads us to conclude that systematic errors should be expected, and mechanisms to adjust predictions to account for them should be included in AIS. We introduce two solutions that can adjust for student differences “online” in a running system: one that tracks systemic errors of the learner model (not the student) and adjusts accordingly, and a student-level performance adaptive feature. We demonstrate these solutions’ efficacy and practicality on six large educational datasets and show that these features improved model accuracy in all tested datasets.
Detecting Arabic YouTube Spam Using Data Mining Techniques

Since YouTube became one of the sources of income, the number of users has increased significantly and the number of spammers who aim to spread viruses or to promote their videos and channels. These behaviors have led many YouTubers to close their channels or to disable the comments because YouTube does not have enough tools to prevent it. Filtering Arabic spam comments is a big challenge at all according to various dialects which hold a huge number of synonyms. In this work, we have classified these comments using different algorithms such as Decision Tree(DT), Support Vector Machine (SVM), Naive Bayes(NB), Random Forest, and k-Nearest Neighbor (k-NN).
Multi-View Federated Learning with Data Collaboration

Under the privacy protection policy, federated learning has received more and more attention. Vertical federated learning (VFL) uses the same samples local in different parties to build prediction model. However, the same samples (overlapping samples) may be limited, while a large number of non-overlapping samples in each party are not utilized. If the non-overlapping samples can be utilized for training, it can benefit the prediction model. In this paper, we propose a novel VFL method, called Multi-View Federated Learning with Data collaboration (FedMC), to solve the problem of insufficient overlapping samples by exploiting suitable non-overlapping samples for data training. The proposed FedMC method first constructs a common feature space based on the overlapping samples, then projects the non-overlapping samples into the common feature space. We measure the similarity for each pair of the non-overlapping samples by calculating their distance in this space. When the distance is less than a threshold, we match them and add this pair to the overlapping samples. The expanded overlapping samples are finally used for training to build the prediction model. We evaluate the proposed method on real-world datasets. The experimental results show that the proposed method can improve the classification result by exploiting the non-overlapping samples for training.
Algorithm substitution attacks against receivers

Abstract This work describes a class of Algorithm Substitution Attack (ASA) generically targeting the receiver of a communication between two parties. Our work provides a unified framework that applies to any scheme where a secret key is held by the receiver; in particular, message authentication schemes (MACs), authenticated encryption (AEAD) and public key encryption (PKE). Our unified framework brings together prior work targeting MAC schemes (FSE’19) and AEAD schemes (IMACC’19); we extend prior work by showing that public key encryption may also be targeted. ASAs were initially introduced by Bellare, Paterson and Rogaway in light of revelations concerning mass surveillance, as a novel attack class against the confidentiality of encryption schemes. Such an attack replaces one or more of the regular scheme algorithms with a subverted version that aims to reveal information to an adversary (engaged in mass surveillance), while remaining undetected by users. Previous work looking at ASAs against encryption schemes can be divided into two groups. ASAs against PKE schemes target key generation by creating subverted public keys that allow an adversary to recover the secret key. ASAs against symmetric encryption target the encryption algorithm and leak information through a subliminal channel in the ciphertexts. We present a new class of attack that targets the decryption algorithm of an encryption scheme for symmetric encryption and public key encryption, or the verification algorithm for an authentication scheme. We present a generic framework for subverting a cryptographic scheme between a sender and receiver, and show how a decryption oracle allows a subverter to create a subliminal channel which can be used to leak secret keys. We then show that the generic framework can be applied to authenticated encryption with associated data, message authentication schemes, public key encryption and KEM/DEM constructions. We consider practical considerations and specific conditions that apply for particular schemes, strengthening the generic approach. Furthermore, we show how the hybrid subversion of key generation and decryption algorithms can be used to amplify the effectiveness of our decryption attack. We argue that this attack represents an attractive opportunity for a mass surveillance adversary. Our work serves to refine the ASA model and contributes to a series of papers that raises awareness and understanding about what is possible with ASAs.
FSOU-Net: Feature supplement and optimization U-Net for 2D medical image segmentation

The results of medical image segmentation can provide reliable evidence for clinical diagnosis and treatment. The U-Net proposed previously has been widely used in the field of medical image segmentation. Its encoder extracts semantic features of different scales at different stages, but does not carry out special processing for semantic features of each scale.To improve the feature expression ability and segmentation performance of U-Net, we proposed a feature supplement and optimization U-Net (FSOU-Net).First, we put forward the view that semantic features of different scales should be treated differently. Based on this view, we classify the semantic features automatically extracted by encoders into two categories: shallow semantic features and deep semantic features. Then, we propose the shallow feature supplement module (SFSM), which obtains fine-grained semantic features through up-sampling to supplement the shallow semantic information. Finally, we propose the deep feature optimization module (DFOM), which uses the expansive convolution of different receptive fields to obtain multi-scale features and then performs multi-scale feature fusion to optimize the deep semantic information.The proposed model is experimented on three medical image segmentation public datasets, and the experimental results prove the correctness of the proposed idea. The segmentation performance of the model is higher than the advanced models for medical image segmentation. Compared with baseline network U-NET, the main index of Dice index is 0.75% higher on the RITE dataset, 2.3% higher on the Kvasir-SEG dataset, and 0.24% higher on the GlaS dataset.The proposed method can greatly improve the feature representation ability and segmentation performance of the model.