Zero Knowledge Proofs towards Verifiable Decentralized AI Pipelines

We are witnessing the emergence of decentralized AI pipelines wherein different organisations are involved in the different steps of the pipeline. In this paper, we introduce a comprehensive framework for verifiable provenance for decentralized AI pipelines with support for confidentiality concerns of the owners of data and model assets. Although some of the past works address different aspects of provenance, verifiability, and confidentiality, none of them address all the aspects under one uniform framework. We present an efficient and scalable approach for verifiable provenance for decentralized AI pipelines with support for confidentiality based on zero-knowledge proofs (ZKPs). Our work is of independent interest to the fields of verifiable computation (VC) and verifiable model inference. We present methods for basic computation primitives like read only memory access and operations on datasets that are an order of magnitude better than the state of the art. In the case of verifiable model inference, we again improve the state of the art for de- cision tree inference by an order of magnitude. We present an extensive experimental evaluation of our system

Activity Recognition in Residential Spaces with Internet of Things Devices and Thermal Imaging

In this paper, we design algorithms for indoor activity recognition and 3D thermal model generation using thermal images, RGB images, captured from external sensors, and the internet of things setup. Indoor activity recognition deals with two sub-problems: Human activity and household activity recognition. Household activity recognition includes the recognition of electrical appliances and their heat radiation with the help of thermal images. A FLIR ONE PRO camera is used to capture RGB-thermal image pairs for a scene. Duration and pattern of activities are also determined using an iterative algorithm, to explore kitchen safety situations. For more accurate monitoring of hazardous events such as stove gas leakage, a 3D reconstruction approach is proposed to determine the temperature of all points in the 3D space of a scene. The 3D thermal model is obtained using the stereo RGB and thermal images for a particular scene. Accurate results are observed for activity detection, and a significant improvement in the temperature estimation is recorded in the 3D thermal model compared to the 2D thermal image. Results from this research can find applications in home automation, heat automation in smart homes, and energy management in residential spaces

Topical therapy with clobetasol propionate 0.025% for various dermatological conditions

Topical corticosteroids (TC) are the most commonly prescribed medications for the treatment of several dermatoses. Owing to their potent effect of relieving symptoms, these drugs are indicated for the use of inflammatory and pruritic presentations of dermatologic conditions. Clobetasol propionate (CP) is the most common TC used to treat itching, redness, and swelling caused by some skin conditions. It possesses anti-inflammatory, antipruritic, and vasoconstrictive properties. To exert its effect, CP binds to cytoplasmic glucocorticoid receptors and subsequently activates glucocorticoid receptor-mediated gene expression, thus resulting in the synthesis of certain anti-inflammatory proteins, while inhibiting the synthesis of certain inflammatory mediators. This case series discusses the efficacy, safety, and clinical experience of using CP 0.025% cream for the treatment of different dermatologic conditions

Policy Design for Competitive Retail Electric Institutions: Artificial Intelligence Representations for a Common Property Resource Approach

The U.S. electricity industry is being restructured to increase competition. Although existing policies may lead to efficient wholesale institutions, designing policies for the retail level is more complex because of intricate interactions between individuals and quasi-monopolistic institutions. It is argued that Hirshman's ideas of "exit" and "voice" (Hirshman, 1970) provide powerful abstractions for design of retail institutions. While competition is a known mechanism of "exit," a novel design of the "voice" mechanism is demonstrated through an artificial intelligence (AI) based software process model. The process model of "voice" in retail institutions is designed within the economic context of electricity distribution — a common property resource (CPR), characterized by technological uncertainty and path-dependency. First, it is argued that participant feedback (voice) has to be used effectively to manage the CPR. Further, it is noted that the decision process, of using participant feedback (voice) to incrementally manage uncertainty and path-dependencies, is non-monotonic because it requires the decision makers to often retract previously made assumptions and decisions. An AI based process model of "voice" is developed using an assumption-based truth maintenance system. The model can emulate the non-monotonic decision making process and therefore assist in decision support. Such a systematic framework is flexible, consistent, and easily reorganized as assumptions change. It can provide an effective, formal "voice" mechanism to the retail customers and improve institutional performance

An animal study - underutilized vista of research in dentistry with special reference to biocompatibility of root canal sealer

Background: Endodontic sealers are designed to be used only within the root canal but are frequently extruded through the apical constriction and often placed in intimate contact with periapical tissues for extended periods of time. Hence, assessment of biocompatibility of endodontic sealers is critical to the clinical success of endodontic therapy. Materials and Methods: Eighteen Wistar rats were divided into three groups of 6 each for observation after completion of 14, 30 and 90 days following implantation, respectively. Polyethylene tubes filled with new sealer, and tube without sealer [control] were implanted subcutaneously. The sample subcutaneous tissues from sacrificed rats were analyzed histologically for inflammatory response and were graded with FDI criteria as minimal, moderate and severe. Results were analyzed statistically with Student′s t-test and ANOVA tests. Results : Inflammatory reaction to the polyethylene tube was minimal at 14 and 90 days period and to the new sealer it was severe at 14 days and moderate at 30 and 90 days period. Conclusions: 1. Cytotoxicity of the individual ingredient of the new sealer should be investigated to find out its chemical reaction occurring at tissue interface resulting in persistence of inflammation. 2. This subcutaneous implantation method is a practical method for qualitative evaluation of endodontic material and can yield exact detailed information about tissue reaction of material on a cellular level. 3. Hence, animal study is positive, efficient and valuable method to carry out research successfully in dentistry

Adaptive Flower Pollination Algorithm-Based Energy Efficient Routing Protocol for Multi-Robot Systems

The exploration and mapping of unknown environments, where the reliable exchange of data between the robots and the base station (BS) also plays a pivotal role, are some of the fundamental problems of mobile robotics. The maximum energy of a robot is utilized for navigation and communication. The communication between the robots and the BS is limited by the transmission range and the battery capacity. This situation inflicts constraints while designing an effective communication strategy for a multi-robot system (MRS). The biggest challenge lies in designing a unified framework for navigation and communication of the robots. The underlying notion is to utilize the minimum energy for communication (without limiting the range/efficiency of communication) to ensure that the maximum energy can be used for navigation (for larger area coverage). In this work, we present a communication strategy by using adaptive flower pollination optimization algorithm for MRS in conjunction with simultaneous localization and mapping (SLAM) technique for navigation and map making. The proposed strategy has been compared with multiple routing algorithms in terms of network life time and energy efficiency. The proposed strategy performs 4% better compared with harmony search algorithm (HSA) and approximately 10% better compared with distance aware residual energy-efficient stable election protocol (DARE-SEP) in terms of the total network lifetime when 50% of robots are alive. The performance drastically improves by 20% till the last robot is alive compared with HSA and approximately 26% compared with DARE-SEP. Hence, the energy saved during communication with the utilization of proposed strategy helps the robots explore more areas, which ultimately elevates the efficacy of the whole system

A rare clinicopathological presentation of the breast carcinoma; implications and outcome

In females, the most common cancer is breast carcinoma in which squamous cell carcinoma (SCC) is a rare type of malignancy. Histologically, infiltrating ductal carcinoma is the most common type and lobular, mucinous, and medullary types have lower incidence. Pure SCC of the breast can originate from the skin, nipple, or epithelium of a deep-seated dermoid cyst or squamous metaplasia on chronic inflammation background. We are reporting a rare case of primary SCC of the breast in a 45-year-old female. In follow-up of 8 months, patient is doing well. We discussed our approach for treatment with review of the literature. We have treated this patient successfully with surgical and adjuvant chemotherapy

Towards Design and Feasibility Analysis of DePaaS: AI Based Global Unified Software Defect Prediction Framework

Using artificial intelligence (AI) based software defect prediction (SDP) techniques in the software development process helps isolate defective software modules, count the number of software defects, and identify risky code changes. However, software development teams are unaware of SDP and do not have easy access to relevant models and techniques. The major reason for this problem seems to be the fragmentation of SDP research and SDP practice. To unify SDP research and practice this article introduces a cloud-based, global, unified AI framework for SDP called DePaaS—Defects Prediction as a Service. The article describes the usage context, use cases and detailed architecture of DePaaS and presents the first response of the industry practitioners to DePaaS. In a first of its kind survey, the article captures practitioner’s belief into SDP and ability of DePaaS to solve some of the known challenges of the field of software defect prediction. This article also provides a novel process for SDP, detailed description of the structure and behaviour of DePaaS architecture components, six best SDP models offered by DePaaS, a description of algorithms that recommend SDP models, feature sets and tunable parameters, and a rich set of challenges to build, use and sustain DePaaS. With the contributions of this article, SDP research and practice could be unified enabling building and using more pragmatic defect prediction models leading to increase in the efficiency of software testing

Towards Design and Feasibility Analysis of DePaaS: AI Based Global Unified Software Defect Prediction Framework

Using artificial intelligence (AI) based software defect prediction (SDP) techniques in the software development process helps isolate defective software modules, count the number of software defects, and identify risky code changes. However, software development teams are unaware of SDP and do not have easy access to relevant models and techniques. The major reason for this problem seems to be the fragmentation of SDP research and SDP practice. To unify SDP research and practice this article introduces a cloud-based, global, unified AI framework for SDP called DePaaS—Defects Prediction as a Service. The article describes the usage context, use cases and detailed architecture of DePaaS and presents the first response of the industry practitioners to DePaaS. In a first of its kind survey, the article captures practitioner’s belief into SDP and ability of DePaaS to solve some of the known challenges of the field of software defect prediction. This article also provides a novel process for SDP, detailed description of the structure and behaviour of DePaaS architecture components, six best SDP models offered by DePaaS, a description of algorithms that recommend SDP models, feature sets and tunable parameters, and a rich set of challenges to build, use and sustain DePaaS. With the contributions of this article, SDP research and practice could be unified enabling building and using more pragmatic defect prediction models leading to increase in the efficiency of software testing