Democratizing Chatbot Debugging: A Computational Framework for Evaluating and Explaining Inappropriate Chatbot Responses

Evaluating and understanding the inappropriateness of chatbot behaviors can be challenging, particularly for chatbot designers without technical backgrounds. To democratize the debugging process of chatbot misbehaviors for non-technical designers, we propose a framework that leverages dialogue act (DA) modeling to automate the evaluation and explanation of chatbot response inappropriateness. The framework first produces characterizations of context-aware DAs based on discourse analysis theory and real-world human-chatbot transcripts. It then automatically extracts features to identify the appropriateness level of a response and can explain the causes of the inappropriate response by examining the DA mismatch between the response and its conversational context. Using interview chatbots as a testbed, our framework achieves comparable classification accuracy with higher explainability and fewer computational resources than the deep learning baseline, making it the first step in utilizing DAs for chatbot response appropriateness evaluation and explanation.Comment: 7 pages, 4 figures, accepted to CUI 2023 poster trac

Clinicopathologic features and outcomes following surgery for pancreatic adenosquamous carcinoma

<p>Abstract</p> <p>Background</p> <p>Pancreatic adenosquamous carcinoma (ASC) is a rare pancreatic malignancy subtype. We investigated the clinicopathological features and outcome of pancreatic ASC patients after surgery.</p> <p>Methods</p> <p>The medical records of 12 patients with pancreatic ASC undergoing surgical treatment (1993 to 2006) were retrospectively reviewed. Survival data of patients with stage IIB pancreatic adenocarcinoma and ASC undergoing surgical resection were compared.</p> <p>Results</p> <p>Symptoms included abdominal pain (91.7%), body weight loss (83.3%), anorexia (41.7%) and jaundice (25.0%). Tumors were located at pancreatic head in 5 (41.7%) patients, tail in 5 (41.7%), and body in 4 (33.3%). Median tumor size was 6.3 cm. Surgical resection was performed on 7 patients, bypass surgery on 3, and exploratory laparotomy with biopsy on 2. No surgical mortality was identified. Seven (58.3%) and 11 (91.7%) patients died within 6 and 12 months of operation, respectively. Median survival of 12 patients was 4.41 months. Seven patients receiving surgical resection had median survival of 6.51 months. Patients with stage IIB pancreatic ASC had shorter median survival compared to those with adenocarcinoma.</p> <p>Conclusion</p> <p>Aggressive surgical management does not appear effective in treating pancreatic ASC patients. Strategies involving non-surgical treatment such as chemotherapy, radiotherapy or target agents should be tested.</p

Fuzzy Risk-Based Life Cycle Assessment for Estimating Environmental Aspects in EMS

Environmental aspects plays a central role in environmental management system (EMS) because it is the basis for the identification of an organization-s environmental targets. The existing methods for the assessment of environmental aspects are grouped into three categories: risk assessment-based (RA-based), LCA-based and criterion-based methods. To combine the benefits of these three categories of research, this study proposes an integrated framework, combining RA-, LCA- and criterion-based methods. The integrated framework incorporates LCA techniques for the identification of the causal linkage for aspect, pathway, receptor and impact, uses fuzzy logic to assess aspects, considers fuzzy conditions, in likelihood assessment, and employs a new multi-criteria decision analysis method - multi-criteria and multi-connection comprehensive assessment (MMCA) - to estimate significant aspects in EMS. The proposed model is verified, using a real case study and the results show that this method successfully prioritizes the environmental aspects

MuRAL: Multi-Scale Region-based Active Learning for Object Detection

Obtaining large-scale labeled object detection dataset can be costly and time-consuming, as it involves annotating images with bounding boxes and class labels. Thus, some specialized active learning methods have been proposed to reduce the cost by selecting either coarse-grained samples or fine-grained instances from unlabeled data for labeling. However, the former approaches suffer from redundant labeling, while the latter methods generally lead to training instability and sampling bias. To address these challenges, we propose a novel approach called Multi-scale Region-based Active Learning (MuRAL) for object detection. MuRAL identifies informative regions of various scales to reduce annotation costs for well-learned objects and improve training performance. The informative region score is designed to consider both the predicted confidence of instances and the distribution of each object category, enabling our method to focus more on difficult-to-detect classes. Moreover, MuRAL employs a scale-aware selection strategy that ensures diverse regions are selected from different scales for labeling and downstream finetuning, which enhances training stability. Our proposed method surpasses all existing coarse-grained and fine-grained baselines on Cityscapes and MS COCO datasets, and demonstrates significant improvement in difficult category performance

Enhancement of Cancer Immunotherapy Using Immune Modulating Peptides

poster abstractImmune Peptide Therapeutics (IPT) LLC, an Indiana-based small business and its research partner Indiana University previously identified a novel property of lunasin as a distinct class of immune modulating agent that enhances anti-tumor immunity, which may promote disease-free survival by limiting tumor progression, and thus prolong lives of cancer patients. Lunasin, a synthetic 43-amino acid peptide, was originally isolated from soybeans. Our studies have demonstrated that lunasin exerts robust synergistic effects with cytokines on augmenting IFNγ and granzyme B expression by Natural Killer (NK) cells, which is associated with increased tumoricidal activity of NK cells. In addition, this combination regimen is capable of rescuing IFNγ production ex vivo by NK cells from chemotherapy-treated Non-Hodgkin’s Lymphoma (NHL) patients who are immunocompromised with acquired immune deficiency. The long-term goal is to develop an efficacious immunotherapy which will impact the treatment and improve the clinical outcomes for NHL patients. The dose-response study indicates the optimum concentration of lunasin is at the range of μM, which would undermine its use in clinical studies. To enhance the medicinal value lunasin must be optimized for in vitro and in vivo efficacy. The objective is to develop a second generation of lunasin, which will increase its potency to improve the performance. In this study we have implemented several strategies to design and modify the prototype. The newly developed peptide called IPT.103 has 15 amino acids that are in the D-isoform configuration. Activity of IPT.103 has been tested in vitro with EC50 of 0.78 μM as compared to 4.54 μM for lunasin. IPT.103 also has in vivo activity on enhancing the serum levels of IFNγ production using a mouse model. Taken together, we have developed a peptide derivative (IPT.103) that deviates from its parental type lunasin to increase intellectual merit for commercialization as well as support clinical application

Modulating NK-mediated Immunity by Lunakine

poster abstractDespite the plethora of immune modulating agents available in cancer treatment, their effectiveness relies on a functional immune system. However, the adverse side effects by chemotherapy impede the therapeutic benefits from immunotherapy. It remains a major challenge to prevent relapse for cancer patients who have already undergone rigorous chemotherapy. Lunasin, a 43-amino acid peptide, was originally isolated from soybeans. Our team has recently discovered a novel function of lunasin as an immune modulating agent that exerts robust synergistic effects imposed by several therapeutic cytokines. Such synergism strongly augments IFNγ and granzyme B expression by Natural Killer (NK) cells, which is associated with increased tumoricidal activity. The combination regimen with lunasin and cytokine is capable of restoring NK activation from lymphoma patients with chemotherapy-induced immune dysfunction. Our results support the potential application of lunasin to improve the therapeutic effects of existing cytokine treatment that has been used to eliminate residual tumors cells from lymphoma patients after chemotherapy. We designate lunakine as new formulation by combing lunasin and selected cytokine (filed for US Patent Cooperation Treat). In working with Indiana University and Technology Corporation (IURTC), we have started a startup company, Immune Peptide Therapeutics (IPT), LLC. Our mission is to develop a more efficacious immunotherapy that prevents relapse and confers progression-free survival for cancer patients. With the support from FORCES, our team has successfully developed a second generation of lunasin called IPT.103 that deviates from its parental type. Activity of IPT.103 has been tested in vitro with EC50 of 0.78 μM as compared to 4.54 μM for lunasin, indicating an improved potency to induce IFNγ production by NK cells. The newly developed peptide IPT.103 is expected to strengthen the intellectual property (IP) position for commercialization. We are currently working on tumor models for preclinical assessment of IPT’s regimens in immunotherapy for lymphoma

Learning Fine-Grained Visual Understanding for Video Question Answering via Decoupling Spatial-Temporal Modeling

While recent large-scale video-language pre-training made great progress in video question answering, the design of spatial modeling of video-language models is less fine-grained than that of image-language models; existing practices of temporal modeling also suffer from weak and noisy alignment between modalities. To learn fine-grained visual understanding, we decouple spatial-temporal modeling and propose a hybrid pipeline, Decoupled Spatial-Temporal Encoders, integrating an image- and a video-language encoder. The former encodes spatial semantics from larger but sparsely sampled frames independently of time, while the latter models temporal dynamics at lower spatial but higher temporal resolution. To help the video-language model learn temporal relations for video QA, we propose a novel pre-training objective, Temporal Referring Modeling, which requires the model to identify temporal positions of events in video sequences. Extensive experiments demonstrate that our model outperforms previous work pre-trained on orders of magnitude larger datasets.Comment: BMVC 2022. Code is available at https://github.com/shinying/des