Reviewerly/exHarmony huggingface dataset:
An OWL-Based Specification of Database Management Systems

In the context of a proliferation of Database Management Systems (DBMSs), we have envisioned and produced an OWL 2 ontology able to provide a high-level machine-processable description of the DBMSs domain. This conceptualization aims to facilitate a proper execution of various software engineering processes and database-focused administration tasks. Also, it can be used to improve the decision-making process for determining/selecting the appropriate DBMS, subject to specific requirements. The proposed model describes the most important features and aspects regarding the DBMS domain, including the support for various paradigms (relational, graph-based, key-value, tree-like, etc.), query languages, platforms (servers), plus running environments (desktop, Web, cloud), specific contexts—i.e., focusing on optimizing queries, redundancy, security, performance, schema vs. schema-less approaches, programming languages/paradigms, and others. The process of populating the ontology with significant individuals (actual DBMSs) benefits from the existing knowledge exposed by free and open machine-processable knowledge bases, by using structured data from Wikipedia and related sources. The pragmatic use of our ontology is demonstrated by two educational software solutions based on current practices in Web application development, proving support for learning and experimenting key features of the actual semantic Web technologies and tools. This approach is also an example of using multiple knowledge from database systems, semantic Web technologies, and software engineering areas.
Learning Hierarchical Embedding for Video Instance Segmentation

In this paper, we address video instance segmentation using a new generative model that learns effective representations of the target and background appearance. We propose to exploit hierarchical structural embedding over spatio-temporal space, which is compact, powerful, and flexible in contrast to current tracking-by-detection methods. Specifically, our model segments and tracks instances across space and time in a single forward pass, which is formulated as hierarchical embedding learning. The model is trained to locate the pixels belonging to specific instances over a video clip. We firstly take advantage of a novel mixing function to better fuse spatio-temporal embeddings. Moreover, we introduce normalizing flows to further improve the robustness of the learned appearance embedding, which theoretically extends conventional generative flows to a factorized conditional scheme. Comprehensive experiments on the video instance segmentation benchmark, i.e., YouTube-VIS, demonstrate the effectiveness of the proposed approach. Furthermore, we evaluate our method on an unsupervised video object segmentation dataset to demonstrate its generalizability.
HLS-Based HW/SW Co-Design of the Post-Quantum Classic McEliece Cryptosystem

While quantum computers are rapidly becoming more powerful, the current cryptographic infrastructure is imminently threatened.In a preventive manner, the U.S. National Institute of Standards and Technology (NIST) has initiated a process to evaluate quantum-resistant cryptosystems, to form the first post-quantum (PQ) cryptographic standard.Classic McEliece (CM) is one of the most prominent cryptosystems considered for standardization in NIST's PQ cryptography contest.However, its computational cost poses notable challenges to a big fraction of existing computing devices.This work presents an HLS-based, HW/SW co-design acceleration of the CM Key Encapsulation Mechanism (CM KEM).We demonstrate significant maximum speedups of up to 55.2×, 3.3×, and 8.7× in the CM KEM algorithms of key generation, encapsulation, and decapsulation respectively, comparing to a SW-only scalar implementation.
Reactive Task and Motion Planning under Temporal Logic Specifications

We present a task-and-motion planning (TAMP) algorithm robust against a human operator's cooperative or adversarial interventions. Interventions often invalidate the current plan and require replanning on the fly. Replanning can be computationally expensive and often interrupts seamless task execution. We introduce a dynamically reconfigurable planning methodology with behavior tree-based control strategies toward reactive TAMP, which takes the advantage of previous plans and incremental graph search during temporal logic-based reactive synthesis. Our algorithm also shows efficient recovery functionalities that minimize the number of replanning steps. Finally, our algorithm produces a robust, efficient, and complete TAMP solution. Our experimental results show the algorithm results in superior manipulation performance in both simulated and real-world tasks.
Distilling Bit-level Sparsity Parallelism for General Purpose Deep Learning Acceleration

Along with the rapid evolution of deep neural networks, the ever-increasing complexity imposes formidable computation intensity to the hardware accelerator. In this paper, we propose a novel computing philosophy called “bit interleaving” and the associate accelerator design called “Bitlet” to maximally exploit the bit-level sparsity. Apart from existing bit-serial/parallel accelerators, Bitlet leverages the abundant “sparsity parallelism” in the parameters to enforce the inference acceleration. Bitlet is versatile by supporting diverse precisions on a single platform, including floating-point 32 and fixed-point from 1b to 24b. The versatility enables Bitlet feasible for both efficient inference and training. Empirical studies on 12 domain-specific deep learning applications highlight the following results: (1) up to 81 × /21 × energy efficiency improvement for training/inference over recent high performance GPUs; (2) up to 15 × /8 × higher speedup/efficiency over state-of-the-art fixed-point accelerators; (3) 1.5mm2 area and scalable power consumption from 570mW (float32) to 432mW (16b) and 365mW (8b) @28nm TSMC; (4) highly configurable justified by ablation and sensitivity studies.
Global optimization of objective functions represented by ReLU networks

Neural networks can learn complex, non-convex functions, and it is challenging to guarantee their correct behavior in safety-critical contexts. Many approaches exist to find failures in networks (e.g., adversarial examples), but these cannot guarantee the absence of failures. Verification algorithms address this need and provide formal guarantees about a neural network by answering “yes or no” questions. For example, they can answer whether a violation exists within certain bounds. However, individual “yes or no" questions cannot answer qualitative questions such as “what is the largest error within these bounds”; the answers to these lie in the domain of optimization. Therefore, we propose strategies to extend existing verifiers to perform optimization and find: (i) the most extreme failure in a given input region and (ii) the minimum input perturbation required to cause a failure. A naive approach using a bisection search with an off-the-shelf verifier results in many expensive and overlapping calls to the verifier. Instead, we propose an approach that tightly integrates the optimization process into the verification procedure, achieving better runtime performance than the naive approach. We evaluate our approach implemented as an extension of Marabou, a state-of-the-art neural network verifier, and compare its performance with the bisection approach and MIPVerify, an optimization-based verifier. We observe complementary performance between our extension of Marabou and MIPVerify.
The multi-level classification and regression network for visual tracking via residual channel attention

Siamese networks used for target tracking has attracted widespread attention due to its balanced tracking accuracy and efficient execution speed. However, when there exist similar semantic information in the search area, it is difficult for most Siamese trackers to adapt to the interference of similar semantic information to target localization, which greatly affects the robustness of Siamese trackers. In order to effectively mine feature information and improve localization accuracy, this work proposes one Siamese multi-level classification and regression (SiamMCAR) framework. SiamMCAR first introduces the residual channel attention module into the template branch of Siamese subnetwork. By utilizing the relationship between feature channels to determine the channel weight of the target template feature, making the attention of extracting the template feature is focused on the channel feature of target foreground. Then, one multi-level classification and regression subnetwork containing multiple classification and regression modules is constructed. Feature maps of the output of different classification and regression modules are weighted and fused by using multiple trained weights, which enables the multi-level classification and regression subnetwork to obtain more results of classification and regression of the shallow cross-correlation response map, thereby making the localization more accurate. Extensive experiments on many benchmarks like OTB-50, OTB-100, TC-128, GOT-10K and LaSOT have proved that our SiamMCAR achieves excellent performance and runs at 28 FPS in various challenging tasks of target tracking.
An evolutionary event detection model using the Matrix Decomposition Oriented Dirichlet Process

With the huge expansion of user generated content on social networks, event detection has emerged as a major challenge and source of knowledge discovery. This knowledge is employed in different applications such as recommender systems, crisis management systems, and decision support systems. Dynamicity, overlapping, and evolutionary behavior are the most important issues in event detection. This paper proposes a novel evolutionary model for event detection to capture the dynamism and evolving behavior of events. The proposed method uses a matrix decomposition technique and a Dirichlet Process to detect events and handle their dynamicity. This model consists of two components, namely preliminary event detection and event evolvement tracking. The former component extracts preliminary events from the available data using the matrix decomposition method. Then, subsequent data is employed into a Non-Parametric Bayesian Network, namely Dirichlet Process Mixture Model to evolve the preliminary events. During the evolvement process, data may migrate between extracted events or new events may be discovered. The experimental results and comparisons with several recently developed approaches show the superiority of the proposed approach, and its ability to capture the evolutionary behavior of events over time.
Comparing HIV Post-Exposure Prophylaxis, Testing, and New Diagnoses in Two Australian Cities with Different Lockdown Measures during the COVID-19 Pandemic

Australia introduced a national lockdown on 22 March 2020 in response to the COVID-19 pandemic. Melbourne, but not Sydney, had a second COVID-19 lockdown between July and October 2020. We compared the number of HIV post-exposure prophylaxis (PEP) prescriptions, HIV tests, and new HIV diagnoses during these lockdown periods. The three outcomes in 2020 were compared to 2019 using incidence rate ratio. There was a 37% and 46% reduction in PEP prescriptions in Melbourne and Sydney, respectively, with a larger reduction during lockdown (68% and 57% reductions in Melbourne's first and second lockdown, 60% reduction in Sydney's lockdown). There was a 41% and 32% reduction in HIV tests in Melbourne and Sydney, respectively, with a larger reduction during lockdown (57% and 61% reductions in Melbourne's first and second lockdowns, 58% reduction in Sydney's lockdown). There was a 44% and 47% reduction in new HIV diagnoses in Melbourne and Sydney, respectively, but no significant reductions during lockdown. The reduction in PEP prescriptions, HIV tests, and new HIV diagnoses during the lockdown periods could be due to the reduction in the number of sexual partners during that period. It could also result in more HIV transmission due to substantial reductions in HIV prevention measures during COVID-19 lockdowns.
Multiscale Curvelet Scattering Network

Feature representation has received more and more attention in image classification. Existing methods always directly extract features via convolutional neural networks (CNNs). Recent studies have shown the potential of CNNs when dealing with images' edges and textures, and some methods have been explored to further improve the representation process of CNNs. In this article, we propose a novel classification framework called the multiscale curvelet scattering network (MSCCN). Using the multiscale curvelet-scattering module (CCM), image features can be effectively represented. There are two parts in MSCCN, which are the multiresolution scattering process and the multiscale curvelet module. According to multiscale geometric analysis, curvelet features are utilized to improve the scattering process with more effective multiscale directional information. Specifically, the scattering process and curvelet features are effectively formulated into a unified optimization structure, with features from different scale levels being efficiently aggregated and learned. Furthermore, a one-level CCM, which can essentially improve the quality of feature representation, is constructed to be embedded into other existing networks. Extensive experimental results illustrate that MSCCN achieves better classification accuracy when compared with state-of-the-art techniques. Eventually, the convergence, insight, and adaptability are evaluated by calculating the trend of loss function's values, visualizing some feature maps, and performing generalization analysis.
Optimizing Sensor Position with Virtual Sensors in Human Activity Recognition System Design

Human activity recognition (HAR) systems combined with machine learning normally serve users based on a fixed sensor position interface. Variations in the installing position will alter the performance of the recognition and will require a new training dataset. Therefore, we need to understand the role of sensor position in HAR system design to optimize its effect. In this paper, we designed an optimization scheme with virtual sensor data for the HAR system. The system is able to generate the optimal sensor position from all possible locations under a given sensor number. Utilizing virtual sensor data, the training dataset can be accessed at low cost. The system can help the decision-making process of sensor position selection with great accuracy using feedback, as well as output the classifier at a lower cost than a conventional training model.
Random Walks on Huge Graphs at Cache Efficiency

Data-intensive applications dominated by random accesses to large working sets fail to utilize the computing power of modern processors. Graph random walk, an indispensable workhorse for many important graph processing and learning applications, is one prominent case of such applications. Existing graph random walk systems are currently unable to match the GPU-side node embedding training speed.
Uncovering In-DRAM RowHammer Protection Mechanisms:A New Methodology, Custom RowHammer Patterns, and Implications

The RowHammer vulnerability in DRAM is a critical threat to system security. To protect against RowHammer, vendors commit to security-through-obscurity: modern DRAM chips rely on undocumented, proprietary, on-die mitigations, commonly known as Target Row Refresh (TRR). At a high level, TRR detects and refreshes potential RowHammer-victim rows, but its exact implementations are not openly disclosed. Security guarantees of TRR mechanisms cannot be easily studied due to their proprietary nature.
A Case Study of Phishing Incident Response in an Educational Organization

Malicious communications aimed at tricking employees are a serious threat for organizations, necessitating the creation of procedures and policies for quickly respond to ongoing attacks. While automated measures provide some protection, they cannot completely protect an organization. In this case study, we use interviews and observations to explore the processes staff at a large University use when handling reports of malicious communication, including how the help desk processes reports, whom they escalate them to, and how teams who manage protections such as the firewalls and mail relays use these reports to improve defenses. We found that the process and work patterns are a distributed cognitive process requiring multiple distinct teams with narrow system access and tactic knowledge. Sudden large campaigns were found to overwhelm the help desk with reports, greatly impacting staff's workflow and hindering the effective application of mitigations and the potential for reflection. We detail potential improvements to ticketing systems and reflect on ITIL, a common framework of best practice in IT management.
Multi-Keyword Ranked Searchable Encryption with the Wildcard Keyword for Data Sharing in Cloud Computing

Abstract Multi-keyword ranked searchable encryption (MRSE) supports multi-keyword contained in one query and returns the top-k search results related to the query keyword set. It realized effective search on encrypted data. Most previous works about MRSE can only make the complete keyword search and rank on the server-side. However, with more practice, users may not be able to express some keywords completely when searching. Server-side ranking increases the possibilities of the server inferring some keywords queried, leading to the leakage of the user’s sensitive information. In this paper, we propose a new MRSE system named ‘multi-keyword ranked searchable encryption with the wildcard keyword (MRSW)’. It allows the query keyword set to contain a wildcard keyword by using Bloom filter (BF). Using hierarchical clustering algorithm, a clustering Bloom filter tree (CBF-Tree) is constructed, which improves the efficiency of wildcard search. By constructing a modified inverted index (MII) table on the basis of the term frequency-inverse document frequency (TF-IDF) rule, the ranking function of MRSW is performed by the user. MRSW is proved secure under adaptive chosen-keyword attack (CKA2) model, and experiments on a real data set from the web of science indicate that MRSW is efficient and practical.
Adaptive LiDAR Sampling and Depth Completion Using Ensemble Variance

This work considers the problem of depth completion, with or without image data, where an algorithm may measure the depth of a prescribed limited number of pixels. The algorithmic challenge is to choose pixel positions strategically and dynamically to maximally reduce overall depth estimation error. This setting is realized in daytime or nighttime depth completion for autonomous vehicles with a programmable LiDAR. Our method uses an ensemble of predictors to define a sampling probability over pixels. This probability is proportional to the variance of the predictions of ensemble members, thus highlighting pixels that are difficult to predict. By additionally proceeding in several prediction phases, we effectively reduce redundant sampling of similar pixels. Our ensemble-based method may be implemented using any depth-completion learning algorithm, such as a state-of-the-art neural network, treated as a black box. In particular, we also present a simple and effective Random Forest-based algorithm, and similarly use its internal ensemble in our design. We conduct experiments on the KITTI dataset, using the neural network algorithm of Ma et al. and our Random Forest based learner for implementing our method. The accuracy of both implementations exceeds the state of the art. Compared with a random or grid sampling pattern, our method allows a reduction by a factor of 4-10 in the number of measurements required to attain the same accuracy.
Anti-occlusion face recognition algorithm based on a deep convolutional neural network

As an essential subproblem in the face recognition field, occluded face recognition has received considerable attention in recent years. However, satisfactory recognition accuracy for occluded faces has not been achieved. Therefore, in order to solve this problem, this paper proposes a convolutional neural network that has multidimensional serial feature extraction modules for occluded faces and uses the deep learning method to improve the recognition rate. In order to improve the expression ability of the network, the method extracts features from two dimensions — space and channel — and built a multidimensional serial feature extraction module. By "multiscale" and "dependence" processing features, the generalization ability was significantly improved. Through the experimental test, the recognition accuracy of the multidimensional feature network (MFNet) reached 90.35%. This accuracy is 17.89% (72.36%) better than that of the traditional algorithm. Meanwhile, compared with other convolutional neural networks, this network has different degrees of improvement, which are 0.68% (ArcFace), 2.14% (Visual Geometry Group Face), and 4.83% (DeepID3).
Bidl

A permissioned blockchain framework typically runs an efficient Byzantine consensus protocol and is attractive to deploy fast trading applications among a large number of mutually untrusted participants (e.g., companies). Unfortunately, all existing permissioned blockchain frameworks adopt sequential workflows for invoking the consensus protocol and executing applications' transactions, making the performance of these applications much lower than deploying them in traditional systems (e.g., in-datacenter stock exchange).
Improving the Movement Synchrony Estimation with Action Quality Assessment in Children Play Therapy

Movement synchrony refers to the dynamic temporal connection between the motions of interacting people. The automatic measurement of movement synchrony is worth studying for social behavior analysis applications, for instance, in play therapy of children in the autism spectrum. Existing approaches based on motion energy analysis are strongly reliant on the region of interest, and thus limit the interaction between individuals, especially for highly engaging activities like play therapy. Inspired by action quality assessment, a task to assess how well an action has been performed, in this paper, we propose an end-to-end deep learning method to integrate the following major tasks: (1) the automatic assessment of children's performance in play therapy, and (2) the automatic estimation of movement synchrony between children and therapists, facilitated by an auxiliary task of intervention activity recognition. This multi-task paradigm generally improves the performance of our model across all tasks. Furthermore, when annotations are subjective, the typical exclusive annotation strategy may reduce tagging quality. As a result, we explored applying distribution learning to mitigate human bias in movement synchrony estimation. We allowed the second and third labels for each instance, namely the uncertainty-preserved annotation approach. We tested our method on Play Therapy 13 (PT13), a dataset collected from video recordings of play therapy interventions. The findings of the experiments indicated that our framework can accurately quantify movement synchronization and assess the quality of children's actions in play therapy. Moreover, the uncertainty-preserved annotation approach produced a comparable outcome to standard methods at a far reduced cost, demonstrating its efficacy in mitigating biases.