Reviewerly/exHarmony huggingface dataset:
A New Approach to the Kasami Codes of Type 2

The dual of the Kasami code of length $q^2-1$, with $q$ a power of $2$, is constructed by concatenating a cyclic MDS code of length $q+1$ over $F_q$ with a Simplex code of length $q-1$. This yields a new derivation of the weight distribution of the Kasami code, a new description of its coset graph, and a new proof that the Kasami code is completely regular. The automorphism groups of the Kasami code and the related $q$-ary MDS code are determined. New cyclic completely regular codes over finite fields a power of $2$, generalized Kasami codes, are constructed; they have coset graphs isomorphic to that of the Kasami codes. Another wide class of completely regular codes, including additive codes, as well as unrestricted codes, is obtained by combining cosets of the Kasami or generalized Kasami code.
Evolution of Large-scale Systems in the Universal Algorithmic Space of Digital Transformation

The article presents an analysis of the patterns of development of the global computer environment (GCE) and its impact on the evolution of large-scale systems. The identification and elimination of the fundamental reasons limiting the further expansion of the fields of application of such systems open the way to the formation of a universal algorithmic space for the digital transformation of large-scale systems in the GCE without fundamental restrictions on their sizes.
WITHDRAWN: Improving privacy in SDN based MANET using hybrid encryption and decryption algorithm

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Hameem Shanavas, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.
Ethics and Cloud Computing

Abstract While the benefits of cloud computing are widely acknowledged, it raises a range of ethical concerns. The extant cloud computing literature reports specific ethical perspectives on focussed topics in this domain, but does not explicitly refer to a specific ethical conception or reference point. This chapter provides an overview of ethics and ethical theories, which can be used to analyse the use of cloud technology and the complex multi-stakeholder structure of the industry. It is critical that cloud providers and users recognise that they effectively shape the morality of the cloud computing context through their interactions with other providers and users, and with the platform itself. Both stakeholder sets must be accountable for the possibilities offered by the technology. While pertinent regulation is continuously evolving, it is unlikely to advance at a similar rapid pace to that of innovation in the cloud computing industry. It is therefore essential that ethics is carefully considered to orient cloud computing towards the good of society.
Control Barrier Function based on Point Cloud for Human Assist Control

Human assist control has also attracted much attention for a system driven by a human operator in recent years. Human assist controllers based on control barrier functions has been proposed. Control barrier functions enable the actual system input to satisfy safety requirements. In this research, we propose a human assist control method using a control barrier function for collision-free locomotion. Especially, this paper focuses on direct use of point cloud data obtained from a laser range scanner. As a result, the proposed controller achieves both collision avoidance and severe approaching to obstacles. Furthermore, it can also take account of the robot shape.
A Game AI Competition to Foster Collaborative AI Research and Development

Game AI competitions are important to foster research and development on Game AI and AI in general. These competitions supply different challenging problems that can be translated into other contexts, virtual or real. They provide frameworks and tools to facilitate the research on their core topics and provide means for comparing and sharing results. A competition is also a way to motivate new researchers to study these challenges. In this document, we present the Geometry Friends Game AI Competition. Geometry Friends is a two-player cooperative physics-based puzzle platformer computer game. The concept of the game is simple, though its solving has proven to be difficult. While the main and apparent focus of the game is cooperation, it also relies on other AI-related problems such as planning, plan execution, and motion control, all connected to situational awareness. All of these must be solved in real-time. In this paper, we discuss the competition and the challenges it brings, and present an overview of the current solutions.
Elastic Registration Based on Compliance Analysis and Biomechanical Graph Matching

An automatic elastic registration method suited for vascularized organs is proposed. The vasculature in both the preoperative and intra-operative images is represented as a graph. A typical application of this method is the fusion of pre-operative information onto the organ during surgery, to compensate for the limited details provided by the intra-operative imaging modality (e.g. CBCT) and to cope with changes in the shape of the organ. Due to image modalities differences and organ deformation, each graph has a different topology and shape. The Adaptive Compliance Graph Matching (ACGM) method presented does not require any manual initialization, handles intra-operative nonrigid deformations of up to 65 mm and computes a complete displacement field over the organ from only the matched vasculature. ACGM is better than the previous Biomechanical Graph Matching method 3 (BGM) because it uses an efficient biomechanical vascularized liver model to compute the organ's transformation and the vessels bifurcations compliance. This allows to efficiently find the best graph matches with a novel compliance-based adaptive search. These contributions are evaluated on ten realistic synthetic and two real porcine automatically segmented datasets. ACGM obtains better target registration error (TRE) than BGM, with an average TRE in the real datasets of 4.2 mm compared to 6.5 mm, respectively. It also is up to one order of magnitude faster, less dependent on the parameters used and more robust to noise.
Unsupervised Adversarial Defense through Tandem Deep Image Priors

Deep neural networks are vulnerable to the adversarial example synthesized by adding imperceptible perturbations to the original image but can fool the classifier to provide wrong prediction outputs. This paper proposes an image restoration approach which provides a strong defense mechanism to provide robustness against adversarial attacks. We show that the unsupervised image restoration framework, deep image prior, can effectively eliminate the influence of adversarial perturbations. The proposed method uses multiple deep image prior networks called tandem deep image priors to recover the original image from adversarial example. Tandem deep image priors contain two deep image prior networks. The first network captures the main information of images and the second network recovers original image based on the prior information provided by the first network. The proposed method reduces the number of iterations originally required by deep image prior network and does not require adjusting the classifier or pre-training. It can be combined with other defensive methods. Our experiments show that the proposed method surprisingly achieves higher classification accuracy on ImageNet against a wide variety of adversarial attacks than previous state-of-the-art defense methods.
The generalization error of graph convolutional networks may enlarge with more layers

Graph Neural Networks(GNNs) are powerful methods to analyze the non-Euclidean data. As a dominant type of GNN, Graph Convolutional Networks(GCNs) have wide applications. However, the analysis of the generalization error for GCNs with multilayer is limited. Based on the review of single-layer GCNs, this paper analyzes the generalization error of two-layers GCNs and extends the conclusion to the general GCNs models. Firstly, this paper examines two-layers GCNs and obtains the stability of the GCNs algorithm. And then, based on this algorithmic stability, the generalization stability of multilayer GCNs is obtained. This paper shows that the algorithmic stability of GCNs depends upon the graph filters and its product with node features as well as the training procedure. Furthermore, the generalization error gap of GCNs tends to be enlarged with more layers, which can interpret why GCNs with deeper layers have relatively poorer performance in test datasets.
An Efficient Sentiment Analysis on Feedback Assessment from Student to Provide Better Education

Opinion mining concept is deployed to predict the trainer evaluation with student's feedback. To examine this feedback concept, where opinion examination helps to distinguish how students are communicated in writings and whether the articulations demonstrate positive (ideal) or negative (troublesome) and conclusions toward the subject. In this research work efficient fusion based neural network (EF-NN) classifier is introduced to predict the frequent context patterns used in the student feedback dataset. Our proposed EF-NN is a hybrid model of both support vector machine and convolutional neural network. Student feedback data set is extracted based on attribute features like the interaction between the students, examination, and notes given, etc., Experimental results can be evaluated on weka toolbox based on this result negative and positive details are collected to improve the efficiency of teaching by faculty to provide the enhanced training. Finally, the result of the accuracy, recall, and precision is compared with the existing K-means clustering method.
Low power and area SHA-256 hardware accelerator on Virtex-7 FPGA

Lately, there have been many technological developments in communication especially in online transactions, so the demand for highly secure systems and cryptographic algorithms has increased. Cryptographic hash functions are used to protect and authenticate information and transactions. SHA-256 (Secure Hash Algorithm-256) is a one-way hash function characterized by being highly secure and fast while having a high collision resistance. This paper presents a new hardware architecture of SHA-256 with low power consumption and area based on a sequential computation of the message scheduler and the working variables of SHA-256. The hardware was described in HDL and implemented on Virtex-7 FPGA which offers high efficiency and speed. Different optimization techniques were used to further reduce the power and area such as gated clock conversion, arithmetic resource sharing, and structural modeling of small building blocks. The proposed design ran with a maximum frequency of 83.33 MHz. The implementation reports indicated a dynamic power consumption of 13 mW and area utilization of 275 slices while maintaining a good throughput of 0.637 Gbits/s and a relatively high efficiency of 2.32 Mbits/s per slice. Such design with low power and area can be used to hash messages on a portable device opening a whole new area for different applications and opportunities.
Formal reasoning of knowledge in systems engineering through epistemic modal logic

Abstract Large‐scale systems engineering projects consist of hundreds to thousands of agents (stakeholders, managers, designers, etc.) spread across the organizational hierarchy. Each of these agents possesses knowledge in some form or the other, be it the knowledge of stakeholder needs, domain‐specific knowledge, knowledge of rules and regulations, knowledge gained from experience on previous projects, etc. It is important to formally represent this knowledge possessed by various agents in the systems engineering lifecycle, as this will enable knowledge reuse, reasoning capabilities, inferring new knowledge, and mitigate misinterpretation of knowledge as prevalent in document‐centric approaches. Such a formal representation will also help alleviate some of the challenges posed by the current document‐centric approaches concerning project delays and cost overruns in the development of large‐scale systems. This paper focuses on formally representing knowledge that exists in various phases of the systems engineering lifecycle by leveraging epistemic modal logic. Descriptive examples are used to demonstrate the practical reasoning capabilities of such a formal representation of knowledge in systems engineering projects.
Adaptive monitoring for autonomous vehicles using the HAFLoop architecture

Current Self-Adaptive Systems (SASs) such as Autonomous Vehicles (AVs) are systems able to deal with highly complex contexts. However, due to the use of static feedback loops they are not able to respond to unanticipated situations such as sensor faults. Previously, we have proposed HAFLoop (Highly Adaptive Feedback control Loop), an architecture for adaptive loops in SASs. In this paper, we incorporate HAFLoop into an AV solution that leverages machine learning techniques to determine the best monitoring strategy at runtime. We have evaluated our solution using real vehicles. Evaluation results are promising and demonstrate the great potential of our proposal.
Trustworthy Dynamic Target Detection and Automatic Monitor Scheme for Mortgage Loan with Blockchain-Based Smart Contract

Driven by technologies such as deep learning and blockchain, financial service industry has made rapid improvements in recent years. Mortgage loan is the main profit-making means of commercial banks, but there are many problems such as manpower cost, insufficient accuracy and evaluation of collateral. To cope with those problem, Artificial Intelligence (AI) and blockchain have been widely exploited. However, the monitoring of collateral is image recognition in dynamic video and it is difficult to avoid the risk of security attack. In this paper, we design a blockchain empowered method to achieve dynamic target detection and automatic monitor, which the change of target location and quantity can be detected by deep learning and recorded in blockchain. Meanwhile, if risk is detected, it will automatically trigger the alarm smart contract to realize automatic monitoring. We further implement a software prototype on fabric framework with real-world scenarios. Experiment results from real-world images show that the feasibility, usability and scalability of our proposed dynamic target detection and automatic monitor scheme for mortgage loan.
Cogmic space for narrative-based world representation

Representing a world or a physical/social environment in an agent’s cognitive system is essential for creating human-like artificial intelligence. This study takes a story-centered approach to this issue. In this context, a story refers to an internal representation involving a narrative structure, which is assumed to be a common form of organizing past, present, future, and fictional events and situations. In the artificial intelligence field, a story or narrative is traditionally treated as a symbolic representation. However, a symbolic story representation is limited in its representational power to construct a rich world. For example, a symbolic story representation is unfit to handle the sensory/bodily dimension of a world. In search of a computational theory for narrative-based world representation, this study proposes the conceptual framework of a Cogmic Space for a comic strip-like representation of a world. In the proposed framework, a story is positioned as a mid-level representation, in which the conceptual and sensory/bodily dimensions of a world are unified. The events and their background situations that constitute a story are unified into a sequence of panels. Based on this structure, a representation (i.e., a story) and the represented environment are connected via an isomorphism of their temporal, spatial, and relational structures. Furthermore, the framework of a Cogmic Space is associated with the generative aspect of representations, which is conceptualized in terms of unconscious- and conscious-level processes/representations. Finally, a proof-of-concept implementation is presented to provide a concrete account of the proposed framework.
Visual-Depth Matching Network: Deep RGB-D Domain Adaptation With Unequal Categories

Existing domain adaptation (DA) methods generally assume that different domains have identical label space, and the training data are only sampled from a single domain. This unrealistic assumption is quite restricted for real-world applications, since it neglects the more practical scenario, where the source domain can contain the categories that are not shared by the target domain, and the training data can be collected from multiple modalities. In this article, we address a more difficult but practical problem, which recognizes RGB images through training on RGB-D data under the label space inequality scenario. There are three challenges in this task: 1) source and target domains are affected by the domain mismatch issue, which results in that the trained models perform imperfectly on the test data; 2) depth images are absent in the target domain (e.g., target images are captured by smartphones), when the source domain contains both the RGB and depth data. It makes the ordinary visual recognition approaches hardly applied to this task; and 3) in the real world, the source and target domains always have different numbers of categories, which would result in a negative transfer bottleneck being more prominent. Toward tackling the above challenges, we formulate a deep model, called visual-depth matching network (VDMN), where two new modules and a matching component can be trained in an end-to-end fashion jointly to identify the common and outlier categories effectively. The significance of VDMN is that it can take advantage of depth information and handle the domain distribution mismatch under label inequality simultaneously. The experimental results reveal that VDMN exceeds the state-of-the-art performance on various DA datasets, especially under the label inequality scenario.
Revisiting the Prefer-same and Prefer-opposite de Bruijn sequence constructions

We present a simple greedy algorithm to construct the prefer-same de Bruijn sequence and prove that it is equivalent to the more complex algorithm first stated by Eldert et al. without proof (Eldert et al., 1958 [3]), and later by Fredricksen (Fredricksen, 1982 [5]). Then we prove that the resulting sequence has the lexicographically largest run-length representation among all de Bruijn sequences. Furthermore, we prove that the sequence resulting from a prefer-opposite greedy construction has the lexicographically smallest run-length representation among all de Bruijn sequences.
Communication-Efficient Decentralized Local SGD over Undirected Networks

We consider the distributed learning problem where a network of n agents seeks to minimize a global function F. Agents have access to F through noisy gradients, and they can locally communicate with their neighbors over an undirected network. We study the Decentralized Local SGD method, where agents perform a number of local gradient steps and occasionally exchange information with their neighbors. Previous algorithmic analysis efforts have focused on the specific network topology (star topology), where a leader node aggregates all agents’ information. We generalize that setting to an arbitrary undirected network by analyzing the trade-off between the number of communication rounds and the computational effort of each agent. We bound the expected optimality gap in terms of the number of iterates T, the number of workers n, and the spectral gap of the underlying network. Our main results show that by using only R = Ω(n) communication rounds, one can achieve an error that scales as O(1/nT), where the number of communication rounds is independent of T and only depends on the number of agents.
Existential active integrity constraints

Active integrity constraints (AICs) are a useful formalism to express integrity constraints and policies to restore consistency in databases violating them. However, AICs do not allow users to express different kinds of constraints commonly arising in practice, such as foreign keys. In this paper, we propose existential active integrity constraints (EAICs), a powerful extension of AICs that allows us to express a wide range of constraints used in databases and ontological systems. We investigate different properties of EAICs. Specifically, we show that there exists a “representative” set of founded updates, called universal, which suffices for query answering. As such a set might contain an infinite number of founded updates, each of infinite size, we study syntactic restrictions ensuring finiteness, as well as the existence of a single universal founded update.
Autonomous Face Detection and Tracking Using Quadrotor UAV

In this paper, human face detection and tracking system with a camera of the Quadrotor UAV is proposed. During flight, Quadrotor takes photos of the human face, records videos and sends these photos and videos to the computer with Wi-fi connection. Face detection algorithm detects human face using Viola Jones algorithm. Face detection algorithm can detect multiple faces at the same time. Face tracking algorithm identifies feature points of the face in first frame and track these features in following frames in a video recorded by the Quadrotor UAV's camera. Face detection and face tracking algorithms' performances are evaluated by using photos and videos of the human faces. It could be interpreted that face detection algorithm successfully detect single and multiple faces and face tracking algorithm is competent to track the human face.
A Contextual Anomaly Detection Framework for Energy Smart Meter Data Stream

Monitoring abnormal energy consumption is helpful for demand-side management. This paper proposes a framework for contextual anomaly detection (CAD) for residential energy consumption. This framework uses a sliding window approach and prediction-based detection method, along with the use of a concept drift method to identify the unusual energy consumption in different contextual environments. The anomalies are determined by a statistical method with a given threshold value. The paper evaluates the framework comprehensively using a real-world data set, compares with other methods and demonstrates the effectiveness and superiority.