HYBRID DATA APPROACH FOR SELECTING EFFECTIVE TEST CASES DURING THE REGRESSION TESTING

In the software industry, software testing becomes more important in the entire software development life cycle. Software testing is one of the fundamental components of software quality assurances. Software Testing Life Cycle (STLC)is a process involved in testing the complete software, which includes Regression Testing, Unit Testing, Smoke Testing, Integration Testing, Interface Testing, System Testing & etc. In the STLC of Regression testing, test case selection is one of the most important concerns for effective testing as well as cost of the testing process. During the Regression testing, executing all the test cases from existing test suite is not possible because that takes more time to test the modified software. This paper proposes new Hybrid approach that consists of modified Greedy approach for handling the test case selection and Genetic Algorithm uses effective parameter like Initial Population, Fitness Value, Test Case Combination, Test Case Crossover and Test Case Mutation for optimizing the tied test suite. By doing this, effective test cases are selected and minimized the tied test suite to reduce the cost of the testing process. Finally the result of proposed approach compared with conventional greedy approach and proved that our approach is more effective than other existing approach

Fast Yet Effective Machine Unlearning

Unlearning the data observed during the training of a machine learning (ML) model is an important task that can play a pivotal role in fortifying the privacy and security of ML-based applications. This paper raises the following questions: (i) can we unlearn a single or multiple classes of data from an ML model without looking at the full training data even once? (ii) can we make the process of unlearning fast and scalable to large datasets, and generalize it to different deep networks? We introduce a novel machine unlearning framework with error-maximizing noise generation and impair-repair based weight manipulation that offers an efficient solution to the above questions. An error-maximizing noise matrix is learned for the class to be unlearned using the original model. The noise matrix is used to manipulate the model weights to unlearn the targeted class of data. We introduce impair and repair steps for a controlled manipulation of the network weights. In the impair step, the noise matrix along with a very high learning rate is used to induce sharp unlearning in the model. Thereafter, the repair step is used to regain the overall performance. With very few update steps, we show excellent unlearning while substantially retaining the overall model accuracy. Unlearning multiple classes requires a similar number of update steps as for the single class, making our approach scalable to large problems. Our method is quite efficient in comparison to the existing methods, works for multi-class unlearning, doesn't put any constraints on the original optimization mechanism or network design, and works well in both small and large-scale vision tasks. This work is an important step towards fast and easy implementation of unlearning in deep networks. We will make the source code publicly available

Is surgery the only option for unstable ankle fracture?

Background: Ankle fracture is one of the most common injuries in sports and daily activity. Unstable ankle fracture that are displaced fracture of the lateral malleolus and most bimalleolar or trimalleolar fractures need surgical reduction and fixation.Methods: It is a single centre study in which all unstable ankle fracture above the age of 18 and not associated with any other injury were included in the study. Following fixation patients were followed up at 6 weeks, 3 months and 6 months and functional outcome was assessed with American Orthopaedic Foot and Ankle score (AOFAS).Results: Supination external rotation injury was most common type. Mean AOFAS score at the end of six months was found to be best in supination adduction type. Posterior malleolus fixation with screw were found to have maximum AOFAS score.Conclusions: Surgical outcome in unstable ankle fracture are proved to have good functional outcome

Dobby: A Conversational Service Robot Driven by GPT-4

This work introduces a robotics platform which embeds a conversational AI agent in an embodied system for natural language understanding and intelligent decision-making for service tasks; integrating task planning and human-like conversation. The agent is derived from a large language model, which has learned from a vast corpus of general knowledge. In addition to generating dialogue, this agent can interface with the physical world by invoking commands on the robot; seamlessly merging communication and behavior. This system is demonstrated in a free-form tour-guide scenario, in an HRI study combining robots with and without conversational AI capabilities. Performance is measured along five dimensions: overall effectiveness, exploration abilities, scrutinization abilities, receptiveness to personification, and adaptability

EBBINNOT: A Hardware Efficient Hybrid Event-Frame Tracker for Stationary Dynamic Vision Sensors

As an alternative sensing paradigm, dynamic vision sensors (DVS) have been recently explored to tackle scenarios where conventional sensors result in high data rate and processing time. This paper presents a hybrid event-frame approach for detecting and tracking objects recorded by a stationary neuromorphic sensor, thereby exploiting the sparse DVS output in a low-power setting for traffic monitoring. Specifically, we propose a hardware efficient processing pipeline that optimizes memory and computational needs that enable long-term battery powered usage for IoT applications. To exploit the background removal property of a static DVS, we propose an event-based binary image creation that signals presence or absence of events in a frame duration. This reduces memory requirement and enables usage of simple algorithms like median filtering and connected component labeling for denoise and region proposal respectively. To overcome the fragmentation issue, a YOLO inspired neural network based detector and classifier to merge fragmented region proposals has been proposed. Finally, a new overlap based tracker was implemented, exploiting overlap between detections and tracks is proposed with heuristics to overcome occlusion. The proposed pipeline is evaluated with more than 5 hours of traffic recording spanning three different locations on two different neuromorphic sensors (DVS and CeleX) and demonstrate similar performance. Compared to existing event-based feature trackers, our method provides similar accuracy while needing approx 6 times less computes. To the best of our knowledge, this is the first time a stationary DVS based traffic monitoring solution is extensively compared to simultaneously recorded RGB frame-based methods while showing tremendous promise by outperforming state-of-the-art deep learning solutions.Comment: 16 pages, 13 figure

Enteral Calcium or Phosphorus Supplementation in Preterm or Low Birth Weight Infants: a Systematic Review and Meta-analysis

OBJECTIVES To assess effects of calcium or phosphorous supplementation compared with no supplementation in human milk-fed preterm or low birth weight infants. METHODS Data sources include Cochrane Central Register of Controlled Trials, Medline and Embase. We included Randomized controlled trials (RCTs) and non-randomized trials (quasi-randomized). RESULTS Three studies (4 reports; 162 infants) were included. At latest follow-up (38 weeks), there was reduction in osteopenia (3 studies, 159 participants, relative risk 0.68, 95% confidence interval [CI] 0.46–0.99). At latest follow-up (6 weeks), there was no effect on weight (1 study, 40 participants, mean difference [MD] 138.50 g, 95% CI −82.16 to 359.16); length (1 study, 40 participants, MD 0.77 cm, 95% CI −0.93 to 2.47); and head circumference (1 study, 40 participants, MD 0.33 cm, 95% CI −0.30 to 0.96). At latest follow-up, there was no effect on alkaline phosphatase (55 weeks) (2 studies, 122 participants, MD −126.11 IU/L, 95% CI −298.5 to 46.27, I2 = 73.4%); serum calcium (6 weeks) (1 study, 40 participants, MD 0.54 mg/dL, 95% CI −0.19 to 1.27); and serum phosphorus (6 weeks) (1 study, 40 participants, MD 0.07 mg/dL, 95% CI −0.22 to 0.36). The certainty of evidence ranged from very low to low. No studies reported on mortality and neurodevelopment outcomes. CONCLUSIONS The evidence is insufficient to determine whether enteral supplementation with calcium or phosphorus for preterm or low birth weight infants who are fed mother's own milk or donor human milk is associated with benefit or harm.publishedVersio

Enteral Vitamin D Supplementation in Preterm or Low Birth Weight Infants: A Systematic Review and Meta-analysis

BACKGROUND AND OBJECTIVES Many preterm and low birth weight (LBW) infants have low vitamin D stores. The objective of this study was to assess effects of enteral vitamin D supplementation compared with no vitamin D supplementation in human milk fed preterm or LBW infants. METHODS Data sources include Cochrane Central Register of Controlled Trials, Medline, and Embase from inception to March 16, 2021. The study selection included randomized trials. Data were extracted and pooled with fixed and random-effects models. RESULTS We found 3 trials (2479 participants) that compared vitamin D to no vitamin D. At 6 months, there was increase in weight-for-age z-scores (mean difference 0.12, 95% confidence interval [CI] 0.01 to 0.22, 1 trial, 1273 participants), height-for-age z-scores (mean difference 0.12, 95% CI 0.02 to 0.21, 1 trial, 1258 participants); at 3 months there was decrease in vitamin D deficiency (risk ratio 0.58, 95% CI 0.49 to 0.68, I2=58%, 2 trials, 504 participants) in vitamin D supplementation groups. However, there was little or no effect on mortality, any serious morbidity, hospitalization, head circumference, growth to 6 years and neurodevelopment. The certainty of evidence ranged from very low to moderate. Fourteen trials (1969 participants) assessed dose and reported no effect on mortality, morbidity, growth, or neurodevelopment, except on parathyroid hormone and vitamin D status. No studies assessed timing. Limitations include heterogeneity and small sample size in included studies. CONCLUSIONS Enteral vitamin D supplementation improves growth and vitamin D status in preterm and LBW infants.publishedVersio

Candidate immunomodulators for COVID-19: Heat-killed Mycobacterium w and BCG vaccine

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