Extracting relevant information from legal documents is a challenging task due to the technical complexity and volume of their content. These factors also increase the costs of annotating large datasets, which are required to train state-of-the-art summarization systems. To address these challenges, we introduce CivilSum, a collection of 23,350 legal case decisions from the Supreme Court of India and other Indian High Courts paired with human-written summaries. Compared to previous datasets such as IN-Abs, CivilSum not only has more legal decisions but also provides shorter and more abstractive summaries, thus offering a challenging benchmark for legal summarization. Unlike other domains such as news articles, our analysis shows the most important content tends to appear at the end of the documents. We measure the effect of this tail bias on summarization performance using strong architectures for long-document abstractive summarization, and the results highlight the importance of long sequence modeling for the proposed task. CivilSum and related code are publicly available to the research community to advance text summarization in the legal domain.

Social media are extensively used in today’s world, and facilitate quick and easy sharing of information, which makes them a good way to advertise products. Influencers of a social media network, owing to their massive popularity, provide a huge potential customer base. However, it is not straightforward to decide which influencers should be selected for an advertising campaign that can generate high returns with low investment. In this work, we present an agent-based model (ABM) that can simulate the dynamics of influencer advertising campaigns in a variety of scenarios and can help discover the best influencer marketing strategy. Our system is a probabilistic graph-based model that provides the additional advantage to incorporate real-world factors such as customers’ interest in a product, customer behavior, the willingness to pay, a brand’s investment cap, influencers’ engagement with influence diffusion, and the nature of the product being advertised, viz. luxury and non-luxury. Using customer acquisition cost and conversion ratio as a unit economic, we evaluate the performance of different kinds of influencers under a variety of circumstances that are simulated by varying the nature of the product and the customers’ interest. Our results exemplify the circumstance-dependent nature of influencer marketing and provide insights into which kinds of influencers would be a better strategy under respective circumstances. For instance, we show that as the nature of the product varies from luxury to non-luxury, the performance of celebrities declines, whereas the performance of nano-influencers improves. In terms of the customers’ interest, we find that the performance of nano-influencers declines with the decrease in customers’ interest, whereas the performance of celebrities improves.

With the advent of electric vehicles (EVs), issues connected to the electric vehicle charging scheduling (EVCS) problem, which is$\textsf {NP}$-hard, have become important. In previous studies, EVCS has been mainly formulated as a constrained shortest-path problem; however, such formulations have not involved variables such as charging rates, traffic congestion, scalability, and waiting time at charging station (CS), that need to be considered in practical settings. Earlier research has also tended to focus on the strengths of particular evolutionary optimization algorithms like differential evolution (DE) or particle swarm optimization (PSO) over others or traditional mathematical programming methods, with only a limited study of hybrid approaches. In this paper, fast and slow charging options at a station are considered in the EVCS problem for practical use. In previous studies, EVs have been considered to have fixed speeds; however, in order to mitigate CS congestion and thus waiting times at CSs, dynamic speed control of EVs has been considered in this work. This work also investigates the scalability of different EVCS solutions. A hybrid approach using PSO and the Firefly algorithm (FFA) with a Lévy flights search strategy is designed and implemented to solve the EVCS. Also, different hybrid methods variants of PSO and FFA have been evaluated in this paper to find the best performing hybrid variant. Experimental results validate the effectiveness of our approach on both synthetic and the real-world transportation networks.

The "Thinking, Fast and Slow" paradigm of Kahneman proposes that we use two different styles of thinking—a fast and intuitive System 1 for certain tasks, along with a slower but more analytical System 2 for others. While the idea of using this two-system style of thinking is gaining popularity in AI and robotics, our work considers how to interleave the two styles of decision-making, i.e., how System 1 and System 2 should be used together. For this, we propose a novel and general framework which includes a new System 0 to oversee Systems 1 and 2. At every point when a decision needs to be made, System 0 evaluates the situation and quickly hands over the decision-making process to either System 1 or System 2. We evaluate such a framework on a modified version of the classic Pac-Man game, with an already-trained RL algorithm for System 1, a Monte-Carlo tree search for System 2, and several different possible strategies for System 0. As expected, arbitrary switches between Systems 1 and 2 do not work, but certain strategies do well. With System 0, an agent is able to perform better than one that uses only System 1 or System 2.

Modelling ethics is critical to understanding and analysing social phenomena. However, prior literature either incorporates ethics into agent strategies or uses it for evaluation of agent behaviour. This work proposes a framework that models both, ethical decision making as well as evaluation using virtue ethics and utilitarianism. In an iteration, agents can use either the classical Continuous Prisoner's Dilemma or a new type of interaction called moral interaction, where agents donate or steal from other agents. We introduce moral interactions to model ethical decision making. We also propose a novel agent type, called virtue agent, parametrised by the agent's level of ethics. Virtue agents' decisions are based on moral evaluations of past interactions. Our simulations show that unethical agents make short term gains but are less prosperous in the long run. We find that in societies with positivity bias, unethical agents have high incentive to become ethical. The opposite is true of societies with negativity bias. We also evaluate the ethicality of existing strategies and compare them with those of virtue agents.

Bundling is a technique e-commerce companies have adopted from traditional retail stores to increase the average order size. It has been observed that bargaining helps increase customer satisfaction while increasing the average order revenue for retailers. We propose a mathematical framework to incorporate bargaining capabilities with the product bundles provided by e-commerce websites. Our method creates a virtual agent that uses the modular Bidding-Opponent-Acceptance model for its bargaining strategy and the Thomas-Kilmann conflict mode instrument to model buyer behavior. We incorporate bargaining capabilities with bundles in an e-commerce system by using a negotiation agent that uses business logic for better strategy. It uses real-time data generated during a negotiation session, since the buyer behavior during a negotiation is crucial. No requirement exists for data from past negotiation sessions of the buyer, which removes bias as well as allowing for rapid changes to buyer behavior. The agent behavior can be altered by various hyperparameters. Our model provides utility metrics to measure buyer and agent satisfaction. Our results show that the agent successfully negotiates with humans from diverse backgrounds.

A problem currently faced by autonomous systems, learning agents, multi-agent systems and other cognitive systems is the extreme susceptibility to bad information and malicious misdirection. In this paper, we propose a strategy to identify trusted agents in a multi-agent system, so that a system can eliminate the knowledge provided by malicious or unknown agents. The strategy is covered in three phases by each agent; firstly sharing information with other agents in the system, secondly calculating the conclusion based on the majority of information received from others and lastly identifying the trusted, malicious and unknown agents in the system. The theoretical analysis shows that the trusted agent strategy (TAS) is capable of identifying malicious or compromised agents if a system has less than 25% of malicious agents. This is further improved in the advanced-TAS which can handle up to 40% malicious agents. The experimental results show that trusted and malicious agents can be identified with high accuracy.

MOTIVATION: Accurate prediction of binding between a major histocompatibility complex (MHC) allele and a peptide plays a major role in the synthesis of personalized cancer vaccines. The immune system struggles to distinguish between a cancerous and a healthy cell. In a patient suffering from cancer who has a particular MHC allele, only those peptides that bind with the MHC allele with high affinity, help the immune system recognize the cancerous cells. RESULTS: MHCAttnNet is a deep neural model that uses an attention mechanism to capture the relevant subsequences of the amino acid sequences of peptides and MHC alleles. It then uses this to accurately predict the MHC-peptide binding. MHCAttnNet achieves an AUC-PRC score of 94.18% with 161 class I MHC alleles, which outperforms the state-of-the-art models for this task. MHCAttnNet also achieves a better F1-score in comparison to the state-of-the-art models while covering a larger number of class II MHC alleles. The attention mechanism used by MHCAttnNet provides a heatmap over the amino acids thus indicating the important subsequences present in the amino acid sequence. This approach also allows us to focus on a much smaller number of relevant trigrams corresponding to the amino acid sequence of an MHC allele, from 9251 possible trigrams to about 258. This significantly reduces the number of amino acid subsequences that need to be clinically tested. AVAILABILITY AND IMPLEMENTATION: The data and source code are available at https://github.com/gopuvenkat/MHCAttnNet.

Drug-drug interaction causes potential impact on patients when a second drug is administered during the duration of action of the first. It may result in the delay or decrease in the absorption of rate of drugs or enhance their absorption. This also in turn may affect the action of drugs or induce adverse effects on patients. There exists a need to study the drug-drug interactions, and their potential effects on the human system, including for drugs not yet approved. This paper proposes using eight features (substructure, targets, transporters, enzymes, pathways, indications, side-effect and off-side-effect, obtained from five different databases: PubChem, Drugbank, KEGG, SIDER, Offsides, and a similarity-based ensemble prediction model to identify the potential drug-drug interactions. The ensemble model uses the Jaccards coefficient method for identifying similarity measures between drugs. This similarity indices are given to a neighbor recommender method and random walk method for the base prediction of drug-drug interaction. This predictive model is improved by an ensemble modrisk and may be of little clinical significance. It can take years to clinically check DDIs for every pair of drugs. Sometimes DDIs may not get detected in clinical trials. Moreover, it may take many years to check DDIs of all known drugs with a newly discovered one before it is introduced to the market. Hence, there exists the need for efficient DDI-checking with fewer time-consuming, expensive, and risky clinical trials.

Automated negotiation is an important class of problems that has wide reaching application in the real world. While a lot of work has been done in Agent-Agent negotiations, Human-Agent negotiations have been relatively unexplored. Human-Agent multi issue bilateral negotiations deals with autonomous agents negotiating with humans over more than one item. Designing agents which can engage in such negotiations requires estimating the preferences of the human opponent in real time and proposing offers which are likely to be accepted before the session timeout. We design an agent that estimates the human opponent's preferences using two new heuristics, Most Changed Least Preferred and Most Offered Most Preferred. Also, the agent utilises the Thomas- Kilmann Conflict Mode Instrument to judge the negotiation strategy of the opponent and then adapts its own strategy to reach agreement faster. The agent does so without the use of historical data, therefore, remaining free from the problems that arise from lack of or biased historical data. Our results show that the agent reaches good agreements against a wide variety of human negotiators. The agreements fall on or near the Pareto-Optimal frontier with the probability of an agreement resulting in an optimal distribution being 97.7 %.

The relationship between tax rates and revenue guides governments in making policy decisions regarding tax reforms. The Laffer curve suggests the possibility of an inverse relationship between tax rates and revenue. There are conflicting views and opinions regarding this relationship and the shape of the Laffer curve. Since the behavioral responses of taxpayers to taxation is convoluted, with individuals having their own preferences, it is possible to use agent-based modeling. This paper creates an agent-based model that simulates taxpayer behavior to explore the relationship between tax rates and revenue. Two fundamental responses to taxation: alteration of labor supply, and tax evasion, are simulated in our agent-based model. The revenue collected for different tax rates is plotted and by doing so, the shape of the Laffer curve is verified. We estimate the unemployment rate for varying tax rates and explain the rationale behind this relationship, and further examine the direct and indirect effects of non-labor income taxes on revenue by plotting the revenue collected against labor income tax rates for different tax rates on non-labor income. To estimate the effect of audit rates on revenue, we analyse the percentage of revenue evaded for different audit rates, and finally numerically estimate the satisfaction of taxpayers at varying tax rates.

Achieving a balance of supply and demand in a multi-agent system with many individual self-interested and rational agents that act as suppliers and consumers is a natural problem in a variety of real-life domains-smart power grids, data centers, and others. In this paper, we address the profit-maximization problem for a group of distributed supplier and consumer agents, with no inter-agent communication. We simulate a scenario of a market with S suppliers and C consumers such that at every instant, each supplier agent supplies a certain quantity and simultaneously, each consumer agent consumes a certain quantity. The information about the total amount supplied and consumed is only kept with the center. The proposed algorithm is a combination of the classical additive-increase multiplicative-decrease (AIMD) algorithm in conjunction with a probabilistic rule for the agents to respond to a capacity signal. This leads to a nonhomogeneous Markov chain and we show almost sure convergence of this chain to the social optimum, for our market of distributed supplier and consumer agents. Employing this AIMD-type algorithm, the center sends a feedback message to the agents in the supplier side if there is a scenario of excess supply, or to the consumer agents if there is excess consumption. Each agent has a concave utility function whose derivative tends to 0 when an optimum quantity is supplied/consumed. Hence when social convergence is reached, each agent supplies or consumes a quantity which leads to its individual maximum profit, without the need of any communication. So eventually, each agent supplies or consumes a quantity which leads to its individual maximum profit, without communicating with any other agents. Our simulations show the efficacy of this approach.

Permanent memory, and biases that humans have such as confirmation bias, are not modeled in some of the leading strategies in Iterated Prisoner's Dilemma. This reduces their effectiveness at modeling actual human behaviors. As a solution to this problem we have proposed a framework to model egoistic agents using trust. This framework accounts for such flaws in human behavior, and provides a more accurate representation of human nature by modeling confirmation bias through egoism and trust. The agents of this model divide their neighboring agents into two sets, I and Non-I, and the actions taken against a neighbor are influenced by the set it belongs to. This framework can be used to modify existing strategies such as Tit-For-Tat to make them more realistic. Using this framework, we have also proposed a trust based strategy and evaluated it. Our strategy does better than the other strategies in pairwise comparisons, and also when there is a combination of different agents in the environment.

In hybrid cloud computing, cloud users have the ability to procure resources from multiple cloud vendors, and furthermore also the option of selecting different combinations of resources. The problem of procuring a single resource from one of many cloud vendors can be modeled as a standard winner determination problem, and there are mechanisms for single item resource procurement given different QoS and pricing parameters. There however is no compatible approach that would allow cloud users to procure arbitrary bundles of resources from cloud vendors. We design the CLOUD-CABOB algorithm to solve the multiple resource procurement problem in hybrid clouds. Cloud users submit their requirements, and in turn vendors submit bids containing price, QoS and their offered sets of resources. The approach is scalable, which is necessary given that there are a large number of cloud vendors, with more continually appearing. We perform experiments for procurement cost and scalability efficacy on the CLOUD-CABOB algorithm using various standard distribution benchmarks like random, uniform, decay and CATS. Simulations using our approach with prices procured from several cloud vendors' datasets show its effectiveness at multiple resource procurement.

During mass evacuations, many psychological and physical factors are responsible for stampedes and other life threatening situations. Quantitative and qualitative analyses of these factors are of high importance while devising optimal strategies for evacuations. In this paper, we present an agent-based model that considers psychological and physical factors that cause panic in such situations. We have also simulated some simple evacuation scenarios and presented a method to identify possible bottlenecks and shortcomings in the environments during emergency evacuations. Our method also helps in evaluation and analysis of different evacuation strategies. To enable this analysis, we have used a rule-based roadmap approach, where critical nodes in the environment are identified by the evacuation planner and each node has a special rule according to the strategy of the planner. We evaluate different strategies on parameters, such as evacuation time and physical discomfort caused to the agents.

In IT service delivery and support, the cloud paradigm has introduced the problem of resource over-provisioning through rapid automation (or orchestration) of manual IT operations. Due to the elastic nature of cloud computing, this shortcoming ends up significantly reducing the real benefit, viz., the cost-effectiveness of cloud adoption for Cloud Service Consumers (CSC). Similarly, detecting and eliminating such over-provisioning of cloud resources without affecting the quality of service (QoS) is extremely difficult for Cloud Service Providers (CSPs) since they have visibility only into the state of the IT services (cloud resources) but none into the actual performance of business services. In this paper, we propose Tier-centric Business Impact and Cost Analysis (T-BICA), a tier-centric optimal resource allocation algorithm, to address the problem of rapid provisioning of IT resources in modern enterprise cloud environments, through extensive data gathering and performance analyses of business services in a simulated environment emulating a mature cloud service provider. We have derived improved analytics to address the issues and to accelerate real cloud adoption for large enterprises within the context of meeting (or exceeding) business service level objectives (SLOs) and minimizing the cloud subscription cost (OpEx) for the business. While investigating the problem, we consider the time and the cost of delivering business service in mediumto large-size enterprise environments, quantifying the negative impact of IT resource over-provisioning (due to highly mature IT services centric orchestration capabilities) on the business, and indicate how the suggested cloud analytics could assist in reducing total cost of ownership (TCO) of the business service. From our analysis of the test data, we have observed that our suggested approach and analytic reduces the cost of delivering business services by 65.19 percent, and improves the performance (total time to deliver) by 74.18 percent when compared to the existing modern cloud management and resource provisioning approach. Using T-BICA dramatically reduces upfront costs (CapEx) for CSPs (from the capacity procurement and management points of view) through efficient on-demand resource de-provisioning, without affecting business SLOs and IT service level agreements (SLAs). The improved dynamic allocation of resources also makes for better efficiency of utilization, which in turn has desirable consequences for sustainability, and makes this an approach for “Green” IT.

Scheduling of tasks on a multi-machine system to reduce the makespan, while satisfying the precedence constraints between the tasks, is known to be an NP-hard problem. We propose a new formulation and show that energy-aware scheduling is a generalization of the minimum makespan scheduling problem. Taking the system graph and program graph as inputs, we propose three different algorithms for energy-aware scheduling, each of them having its own strengths and limitations. The first is a genetic algorithm (Plain GA) that searches for an energy reducing schedule. The second (CA+GA) uses cellular automata (CA) to generate low energy schedules, while using a genetic algorithm (GA) to find good rules for the CA. The third (EAH) is a heuristic which gives preference to high-efficiency machines in allocation. We have tested our algorithms on well-known program graphs and compared our results with other state-of-the-art scheduling algorithms, which confirms the efficacy of our approach. Our work also gives insight into the time-energy trade-offs in scheduling.

We present a cloud resource procurement approach which not only automates the selection of an appropriate cloud vendor but also implements dynamic pricing. Three possible mechanisms are suggested for cloud resource procurement: cloud-dominant strategy incentive compatible (C-DSIC), cloud-Bayesian incentive compatible (C-BIC), and cloud optimal (C-OPT). C-DSIC is dominant strategy incentive compatible, based on the VCG mechanism, and is a low-bid Vickrey auction. C-BIC is Bayesian incentive compatible, which achieves budget balance. C-BIC does not satisfy individual rationality. In C-DSIC and C-BIC, the cloud vendor who charges the lowest cost per unit QoS is declared the winner. In C-OPT, the cloud vendor with the least virtual cost is declared the winner. C-OPT overcomes the limitations of both C-DSIC and C-BIC. C-OPT is not only Bayesian incentive compatible, but also individually rational. Our experiments indicate that the resource procurement cost decreases with increase in number of cloud vendors irrespective of the mechanisms. We also propose a procurement module for a cloud broker which can implement C-DSIC, C-BIC, or C--OPT to perform resource procurement in a cloud computing context. A cloud broker with such a procurement module enables users to automate the choice of a cloud vendor among many with diverse offerings, and is also an essential first step toward implementing dynamic pricing in the cloud.

The cost of electricity contributes significantly to the operating expense incurred in hosting cloud services. It is necessary to consider this cost while charging the consumers for their service utilization. In this work, we arrive at a metering mechanism for cloud services, in which the price of a cloud service tracks the variable input cost of electricity from a smart grid. The power-aware cloud metering developed here is a dynamic pricing and billing model where tariff for a cloud service is varied in accordance with the input electricity cost. We arrive at a model for power consumption of virtual machines hosted on the cloud infrastructure. This power consumption model is used in calculating the cost of operation of the service. A cloud instance leased by a consumer is billed based on the cost of operation obtained, and its resource utilization. Experimental results validate the approach presented.

Growing scale of server infrastructure in large datacenters has led to an increased need for effective server workload prediction mechanisms. Two main challenges faced in server workload prediction task are lack of large-scale training data and changes in the underlying distribution of server workloads in events like change in dominant applications of servers or change in allocation of servers, etc. In this work, we propose an online server workload prediction approach based on ensemble learning which addresses these issues. We evaluate the proposed approach using real dataset of an enterprise data center and a synthetic dataset. Experimental results reveal that the proposed approach achieves accuracy of 87.8% on real dataset and 88.8% on synthetic dataset.

Multiple resource procurement from several cloud vendors participating in bidding is addressed in this paper. This is done by assigning dynamic pricing for these resources. Since we consider multiple resources to be procured from several cloud vendors bidding in an auction, the problem turns out to be one of a combinatorial auction. We pre-process the user requests, analyze the auction and declare a set of vendors bidding for the auction as winners based on the Combinatorial Auction Branch on Bids (CABOB) model. Simulations using our approach with prices procured from several cloud vendors' datasets show its effectiveness in multiple resource procurement in the realm of cloud computing.

Data streams are vast sources of information. In such streams only certain episodic points called events are interesting. The number of events occurring in a data stream can be very large, and a given user would not be interested in all of them. Thus, there is a need to selectively disseminate information about the occurrence of events to users who express interest in them. Publish/Subscribe is a distributed paradigm of messaging in which events published by publishers are automatically sent to subscribers who have expressed interest in them. In this work, we apply the publish/subscribe paradigm to notify a user when an event of interest occurs. Furthermore, user subscriptions may often be vague and non-specific. To capture this uncertainty, we use fuzzy set theory in the modeling of the subscriptions. The use of fuzzy sets gives a user more expressive power in subscriptions and gives better notifications of events.

A unique and efficient system model is proposed for sensor networks to create sensor-smart highways. The approach first considers various cell geometries encountered with 2-D roads and then extends the same to 3-D roads. Mathematical relations describing each type of cell are derived, and the analyses indicate the numbers and positions of sensor nodes needed in each cell of a certain type. We then propose algorithms for computing the deployment positions of sensor nodes on roads by describing them as consisting of cells of different geometries. We also validate the approach by computing a sensor deployment using real geospatial data.

A virtual machine (VM), during its lifecycle, can be scheduled for execution at geographically disparate cloud locations depending upon the cost of computation and the load at these locations. However, trans-locating a live VM across high-latency low-bandwidth wide area networks (WAN) within 'reasonable' time is nearly impossible due to the large size of the VM image. In this paper, we deal with this problem by combining VM scheduling strategies with VM replication strategies. In particular, we propose to replicate a VM image selectively across different cloud sites, choose a replica of the VM image to be the primary copy and propagate the incremental changes at the primary copy to all the remaining replicas of the VM image. The replica placement strategies are based on factors that influence long-term costs such as the average per-unit cost of storage and the average per-unit cost of computation at different cloud sites besides the 'end-user' latency requirements associated with the VMs. We propose to compensate the additional storage requirements due to replication by exploring commonality that naturally exists amongst different VM images using de-duplication techniques. A key issue that naturally arises in this integrated replication and scheduling context for minimizing migration latencies associated with live migration of VMs across WAN is the design of a good replica placement algorithm that minimizes additional storage requirements. In this paper we address this issue as part for our integrated replication and scheduling architecture, called Cloud Spider. We discuss the trade-offs involved in the design of a replica placement algorithm and propose an algorithm that factors in deduplication ratios amongst pairs of VM images while deciding on the question of replica placement of the VM images. Preliminary experiments show extremely promising results.