Reviewerly/exHarmony huggingface dataset:
Influence of Schema Design in NoSQL Document Stores

Schema-free (flexible schema) is an attractive feature in the NoSQL document store, where users can access the database without referring to its schema. It reduces the stress of developers to design database schema and applications. Data modeling is defined as arranging application data using acceptable data structuring patterns and is the primary component of the schema design in the document store. In addition, studies have continued examining NoSQL’s energy consumption pattern. But, our recent survey does not observe any energy-efficienct analysis for NoSQL schema. This work therefore designs alternative schemes using a particular combination of document store structuring patterns and analyzes storage criteria for everything. In NoSQL, the use of indexing and application-level joints is widely adopted. This article also compares performance and energy requirements for query processing with/without indexing, and application-level join operations and also helps to design query optimization.
Effect of payload security in MQTT protocol over transport and application layer

Abstract Digital revolution has made our life dependent on devices that are can be connected to the internet. Hence, making Internet of things (Iot) an inevitable part for now and years to come. The transition from normal devices to those devices that can be connected to internet, has been exponential, in contrast, the technique used by the protocols in those devices to prevent cyber-attacks and keep user data intact has not been in the same pace as that of its usage. This concerns the end user as it risks the data, allowing it to be misused. In this paper we discuss, MQTT (message queue telemetry transport) a lightweight messaging protocol, wherein the payload is acting as the driver for the data to be carried. This payload can include all sort of information, private as well as public. The paper experiments with the vulnerability of the payload when exposed to a cyber-attack, here, Man in the middle (MITM) attack when the same payload is provided with 2 different types of security. Initially at the transport layer and later at the application layer. At first a channel-based security using Transport security layer (TLS) is provided to the payload and later, an object-based security using Advanced Encryption Scheme (AES) is provided to the same payload. Both of these payload encryption technique for MQTT protocol is discussed elaborately.
Global Agendas: Detection of Agenda Shifts in Cross-National Discussions Using Neural-Network Text Summarization for Twitter

Agendas in online media have become a scholarly focus nearly two decades ago, leading to shifting conceptualizations of what we see as agenda. Thus, agendas and agenda shifts inside online discussions have shown its potential to influence offline deliberation, aggregate support, fuel protest, passing through and/or bypassing traditional media's gatekeeping. Real-time (or nearly-real-time) learning about quick agenda movement inside globalized public debate might be particularly important for international organizations like UN or EU. However, we today lack both knowledge on how agendas move in such discussions and instruments on such analysis. In particular, we are next-to-unaware of to what extent globally relevant themes get contextualized within language-based discussion segments, as well as to what extent the latter depend on each other and lag behind each other in developing agendas and public opinion on quickly evolving issues or conflicts. In this paper, we propose a method of agenda detection based on neural-network text summarization and compare summaries of tweet packages across three languages within the Twitter hashtag #jesuischarlie. We show that sentiment detection may allow for quality assessment of the text summaries, as compared to aggregated sentiment to the original tweets. We show that, outside France, agendas were more interpretational, abstract, and non-contextualized. The pattern of news changing to 'issue outburt' was simultaneous in dense discussion segments and lagged behind in a sparser one. We also show that, globally, main issues of the discussion may be spotted within the first hour.
Improving Priority Prediction for Bug Reports with Comment Features

Deciding the priority of processing a bug report is an important issue for the efficiency of software bug resolution. Generally, this decision process is labor-consuming because the triager needs considerable time to decide the priority. To mitigate this problem, an automatic method for predicting the priority of a bug report is needed. In this paper, we propose a novel prediction scheme to improve the prediction performance with comment features. We have conducted empirical experiments to evaluate the proposed scheme with a previous top-ranked scheme eApp. The experimental results show that the proposed approach can effectively improve the prediction performance.
Context Awareness in Enterprise Systems Design

Context awareness concept has evolved over time both in terms of associated technology and of domains of application and is becoming a key issue in designing systems with intelligent behavior, as the capacity of adaptation to environmental changes and contexts is one of the definitory attributes of intelligence. This concept has so far mainly been addressed as process-oriented, thus justifying the large number of existing approaches. However, the formal ones are rather few. It is the objective of this paper to offer a survey covering as large as possible a number of application domains, as a first step in elaborating a set of formally-based guidelines in addressing the topic of context awareness.
Influence of Social Relationships on Decisions in Device-to-Device Communication

Device-to-device (D2D) communication is an innovative solution for improving wireless network performance to efficiently handle the ever-increasing mobile data traffic.Communication takes place directly between two devices that are in each other's transmission range.So far, research has focused on the technical challenges of implementing this technology and assumes a user's general willingness to participate as forwarder in this technology.However, this simplifying assumption is not realistic, as willingness to participate in D2D communication can vary depending on the user.In this work, we consider the scenario that a user can act as a forwarder for a receiver who is not directly or insufficiently reached by the base station and accordingly has no or poor Internet connection.We take a user-centric approach and investigate the willingness to provide an Internet connection as a forwarder.We are the first to investigate user preferences for D2D communication using a choice-based conjoint analysis.Our results, based on a representative sample of potential users (N = 181), show that the social relationship between the potential forwarder and the receiver has the greatest impact on the potential forwarder's decision to provide an Internet connection to the receiver, accepting sacrifices in terms of additional battery consumption and reduced own service performance.In a detailed segment analysis, we observe significant preference differences depending on smartphone usage behavior and user age.Taking the corresponding preferences into account when matching forwarders and receivers can further increase technology adoption.
Resilience and Elasticity for Continuous Service-Based Processes in Pandemic Ages

COVID-19 has turned service-based business continuity into a hot issue, due to the survival of enterprises under long-tailed changes of business caused by various abnormal socioeconomic events and disruptions. We analyze how current techniques enable small and medium enterprises to be resilient and elastic. From observations of service disruptions during COVID-19 and problems with current technologies for resilience and elasticity, we recommend addressing resilience and elasticity requirements through contextual service information and hybrid services coordination programming and serving.
BloomCA: A Memory Efficient Reservoir Computing Hardware Implementation Using Cellular Automata and Ensemble Bloom Filter

In this work, we propose a BloomCA which utilizes cellular automata (CA) and ensemble Bloom filter to organize an RC system by using only binary operations, which is suitable for hardware implementation. The rich pattern dynamics created by CA can map the input into high-dimensional space and provide more features for the classifier. Utilizing the ensemble Bloom filter as the classifier, the features can be memorized effectively. Our experiment reveals that applying the ensemble mechanism to Bloom filter endues a significant reduction in inference memory cost. Comparing with the state-of-the-art reference, the BloomCA achieves a 43× reduction for memory cost without hurting the accuracy. Our hardware implementation also demonstrates that BloomCA achieves over 21× and 43.64% reduction in area and power, respectively.
SilentDelivery: Practical Timed-Delivery of Private Information Using Smart Contracts

This article proposes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SilentDelivery</i> , a secure, scalable, and cost-efficient protocol for implementing timed information delivery service in a decentralized blockchain network. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SilentDelivery</i> employs a novel combination of threshold secret sharing and decentralized smart contracts. The protocol maintains shares of the decryption key of the private information of an information sender using a group of mailman recruited in a blockchain network before the specified future time-frame and restores the information to the information recipient at the required time-frame. To tackle the key challenges that limit the security and scalability of the protocol, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SilentDelivery</i> incorporates two novel countermeasure strategies. The first strategy, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">silent recruitment</i> , enables a mailman to get recruited by a sender <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">silently</i> without the knowledge of any third party. The second strategy, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dual-mode execution</i> , makes the protocol run in a lightweight mode by default, where the cost of running smart contracts is significantly reduced. We rigorously analyze the security of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SilentDelivery</i> and implement the protocol over the Ethereum official test network. The results demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SilentDelivery</i> is more secure and scalable compared to the state of the art and reduces the cost of running smart contracts by 85 percent.
Qualitative Communications for Augmented Reality and Virtual Reality

Qualitative Communication has been proposed to increase the responsiveness of a network due to packet loss. The basic idea is to allow the network to drop part of the payload to preserve the integrity of the session (as opposed to dropping whole packets as in TCP). We consider this idea in the context of AR/VR and see how selectively dropping payload naturally fits with such an application where the data can be easily split within some critical and non-critical data. We present basic mechanism to leverage qualitative communications in 360 degree video streaming, as well as a pre-fetching scheme. We evaluate this proposal on actual 360 video traces and show the significant improvement of our proposal versus a vanilla transmission mechanism.
A highly optimized multi-stage teacher-learner inspired particle swarm optimizer system

The method of Particle Swarm Optimization (PSO) is an epitome in optimization process for solving classification, clustering, and many other multi-dimensional problems. PSO is found to be stronger than few state of the art population-based coup such as Ant colony optimization (ACO), genetic algorithms (GAs), Optimization of the herd of elephants known as Elephant herd optimization (EHO) together with other algorithms under certain real-life scenarios like path finding, feature selection, etc. Originally PSOs were implemented by means of optimizing the velocity/speed and position update equations so that it could locate the best global position of the particles. Many researchers have designed different approaches by combining human behavioral aspects to these equations in order to further optimize the PSOs performance. In this text we have proposed the use of a 4-staged teacher-learning behavior (TLB) inspired PSO for optimizing the system performance. Under different test functions, the proposed algorithm was tested and evaluated and its performance was compared relative with the modern state of the art PSO systems. It is observed that the proposed PSO reduces the convergence time by more than 20%, and also reduces the overall computational complexity to half. We conclude this text by making some acute observations about the implemented system, and recommend methods which can be used for further optimizing the system performance.
Approximate Gradient Coding With Optimal Decoding

Gradient codes use data replication to mitigate the effect of straggling machines in distributed machine learning. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Approximate gradient codes</i> consider codes where the data replication factor is too low to recover the full gradient exactly. Our work is motivated by the challenge of designing approximate gradient codes that simultaneously work well in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">both</i> the adversarial and random straggler models. We introduce novel approximate gradient codes based on expander graphs. We analyze the decoding error both for random and adversarial stragglers, when optimal decoding coefficients are used. With random stragglers, our codes achieve an error to the gradient that decays exponentially in the replication factor. With adversarial stragglers, the error is smaller than any existing code with similar performance in the random setting. We prove convergence bounds in both settings for coded gradient descent under standard assumptions. With random stragglers, our convergence rate improves upon rates obtained via black-box approaches. With adversarial stragglers, we show that gradient descent converges down to a noise floor that scales linearly with the adversarial error to the gradient. We demonstrate empirically that our codes achieve near-optimal error with random stragglers and converge faster than algorithms that do not use optimal decoding coefficients.
Model-Based Approach for Anomaly Detection in Smart Home Inhabitant Daily Life

Due to population ageing, the number of people requiring monitoring and specific health care increased.Nevertheless, due to social or financial reasons, most of this population prefers to stay at home.Thanks to recent improvement in Ambient Assisted Living (AAL) and smart home technologies, new opportunities emerge to develop Health at Home (HaH) solutions.One of the main objectives of HaH is to offer to smart home inhabitants a life and health quality similar to patients in specialized medical institutions.To achieve this task, it is proposed in this paper an approach to monitor frail people at home to detect behavioral changes which can be symptoms of health problems.The method is model-based, working with Stochastic Timed Automata as it is an efficient tool to model activity duration and ordering habits, which are key features of human behavior.Once a model depicting life habits is built, it is proposed to use normalized likelihood and probabilistic distribution to evaluate the consistency between the regular life habits and newly observed behavior.Appearance of a inconsistent behavior may testify the evolution or apparition of a medical symptom.A case study demonstrates the relevancy of the proposed approach, and several scenarios highlight the possibility to have clear enough information on inhabitant behavior to identify a limited number of responsible medical disorder.
Adaptive Cross-Lingual Question Generation with Minimal Resources

Abstract The task of question generation (QG) aims to create valid questions and correlated answers from the given text. Despite the neural QG approaches have achieved promising results, they are typically developed for languages with rich annotated training data. Because of the high annotation cost, it is difficult to deploy to other low-resource languages. Besides, different samples have their own characteristics on the aspects of text contextual structure, question type and correlations. Without capturing these diversified characteristics, the traditional one-size-fits-all model is hard to generate the best results. To address this problem, we study the task of cross-lingual QG from an adaptive learning perspective. Concretely, we first build a basic QG model on a multilingual space using the labelled data. In this way, we can transfer the supervision from the high-resource language to the language lacking labelled data. We then design a task-specific meta-learner to optimize the basic QG model. Each sample and its similar instances are viewed as a pseudo-QG task. The asking patterns and logical forms contained in the similar samples can be used as a guide to fine-tune the model fitly and produce the optimal results accordingly. Considering that each sample contains the text, question and answer, with unknown semantic correlations among them, we propose a context-dependent retriever to measure the similarity of such structured inputs. Experimental results on three languages of three typical data sets show the effectiveness of our approach.
A note on lumped parameter modeling

This brief note examines the lumped dynamic modeling of two simple structural systems: 3D-printed cantilevers, and circular aluminum panels. In each case a set of stiffness and natural frequency measurements are made based on simple experiments, with a view to examining effective mass, and a comparison is made with the basic theory. • Simple expressions given for the natural frequency. • Two different systems considered: a cantilever beam and a circular panel. • Experimental data generated.
Determination of Relevant Hyperspectral Bands Using a Spectrally constrained CNN

For transmitting the large amount of hyperspectral image (HSI) data over a small data link from a small platform to the ground, an efficient data compression with low computational cost has to be done at the platform. Additionally, spectral band reduction interpreted as preprocessing of the compression is reasonable. We present a method for hyperspectral band reduction using a modified convolutional neural network (CNN) which retains the information about the spectral origin from layer to layer until it can be assigned directly to the classes to be classified. The relevant bands for each class are determined. Experimental verification shows that the network architecture using only the relevant bands has improved stability and results in a better overall performance.
Point Based Solution Method for Communicative IPOMDPs

Communicative interactive POMDPs (CIPOMDPs) provide a principled framework for optimal interaction and communication in multi-agent settings by endowing agents with nested models (theories of mind) of others and with the ability to communicate with them. In CIPOMDPs, agents use Bayes update to process their observations and messages without the usual assumption of cooperative discourse. We propose a variant of the point-based value iteration method, called IPBVI-Comm, to compute the approximate optimal policy of a CIPOMDP agent. We then use the IPBVI-Comm to study the optimal communicative behavior of agents in cooperative and competitive scenarios. Unsurprisingly, it is optimal for agents to attempt to mislead if their preferences are not aligned. But it turns out the higher depth of reasoning allows an agent to detect insincere communication and to guard against it. Specifically, in some scenarios, the agent is able to distinguish a truthful friend from a deceptive foe based on the message received.
AI systems and product liability

The article examines whether the current product liability law provides an appropriate regulation for AI systems. This question, which is discussed at the example of the European Product Liability Directive, is of great practical importance in the current legal policy discussion on liability for AI systems.
Unified robot task and motion planning with extended planner using ROS simulator

Safe and efficient exploration of unknown and unstructured environments is critical for an autonomous mobile robot in high-dimensional workspace. Autonomous exploration is essential to safely and robustly build a complete and accurate path in an environment map with obstacles. Task planning tends to find a sequence of discrete actions in solving the symbolic problems. Motion planning finds the optimal path by avoiding the geometric obstacles in the environment. Fusion of these two approaches is advantageous in finding a path in unknown environments. This paper presents a hybrid path planning approach, termed RobMAP (Robotic Motion and Action Planning) for achieving efficient navigation by performing desired tasks in a sequence and thereby generate the optimal path in a large- scale indoor environment. In achieving this, action-planning is done using Planning Domain Definition Language (PDDL) planner through ROSPlan framework and collision-free path is generated using sampling-based Rapidly exploring Random Tree (RRT) algorithm. The ROSPlan system is used in less constrained workspace and it estimates the optimized information gain in exploring each action. Further, the integration of these two methods leads to an intelligent motion planner that searches for an optimal path towards a goal based on the PDDL action planner specification. Extensive experimentation is performed using Robot Operating System (ROS) controlled Rviz tool on a synthetic indoor environment by executing a sequence of actions specified using PDDL. The Turtlebot is equipped with a laser sensor and it explores the environment map with higher precision and accuracy in finding the optimal path. The demonstrated results corroborate that the proposed method of integrating RRT with PDDL provides optimal solutions with lesser path length in much lesser run time. The accuracy, precision and coverage area are higher when compared with other exploration strategies used in the existing work.