Reviewerly/exHarmony huggingface dataset:
Reasoning with Language Model is Planning with World Model

Large language models (LLMs) have shown remarkable reasoning capabilities, particularly with Chain-of-Thought-style prompts. However, LLMs can still struggle with problems that are easy for humans, such as generating action plans for executing tasks or performing complex math or logical reasoning. This is due to LLMs’ absence of an internal world model for predicting world states (e.g., environment status, variable values) and simulating long-term action outcomes of actions. This prevents LLMs from performing deliberate planning akin to human brains, which involves exploring alternative reasoning paths, anticipating future states and rewards, and iteratively refining existing reasoning steps. To overcome the limitations, we propose a new LLM reasoning framework, Reasoning via Planning (RAP). RAP repurposes the LLM as both a world model and a reasoning agent, and incorporates a principled planning algorithm (based on Monte Carlo Tree Search) for strategic exploration in the vast reasoning space. During reasoning, the LLM (as agent) incrementally builds a reasoning tree under the guidance of the LLM (as world model) and task-specific rewards, properly balancing exploration v.s. exploitation to achieve a high-reward reasoning path efficiently. We apply RAP to a variety of challenging reasoning problems, such as plan generation, math reasoning, and logical inference. Empirical results demonstrate the superiority of RAP over various strong baselines, including CoT and least-to-most prompting with self-consistency, e.g., RAP on LLaMA-33B surpasses CoT on GPT-4 with 33% relative improvement in plan generation.
StructGPT: A General Framework for Large Language Model to Reason over Structured Data

In this paper, we aim to improve the reasoning ability of large language models (LLMs) over structured data in a unified way. Inspired by the studies on tool augmentation for LLMs, we develop an Iterative Reading-then-Reasoning (IRR) framework to solve question answering tasks based on structured data, called StructGPT. In this framework, we construct the specialized interfaces to collect relevant evidence from structured data (i.e., reading), and let LLMs concentrate on the reasoning task based on the collected information (i.e., reasoning). Specially, we propose an invoking-linearization-generation procedure to support LLMs in reasoning on the structured data with the help of the interfaces. By iterating this procedure with provided interfaces, our approach can gradually approach the target answers to a given query. Experiments conducted on three types of structured data show that StructGPT greatly improves the performance of LLMs, under the few-shot and zero-shot settings.
Relation-aware Ensemble Learning for Knowledge Graph Embedding

Knowledge graph (KG) embedding is a fundamental task in natural language processing, and various methods have been proposed to explore semantic patterns in distinctive ways. In this paper, we propose to learn an ensemble by leveraging existing methods in a relation-aware manner. However, exploring these semantics using relation-aware ensemble leads to a much larger search space than general ensemble methods. To address this issue, we propose a divide-search-combine algorithm RelEns-DSC that searches the relation-wise ensemble weights independently. This algorithm has the same computation cost as general ensemble methods but with much better performance. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed method in efficiently searching relation-aware ensemble weights and achieving state-of-the-art embedding performance. The code is public at https://github.com/LARS-research/RelEns.
ChatGPT for phenotypes extraction: one model to rule them all?

Information Extraction (IE) is a core task in Natural Language Processing (NLP) where the objective is to identify factual knowledge in textual documents (often unstructured), and feed downstream use cases with the resulting output. In genomic medicine for instance, being able to extract the most precise list of phenotypes associated to a patient allows to improve genetic disease diagnostic, which represents a vital step in the modern deep phenotyping approach. As most of the phenotypic information lies in clinical reports, the challenge is to build an IE pipeline to automatically recognize phenotype concepts from free-text notes. A new machine learning paradigm around large language models (LLM) has given rise of an increasing number of academic works on this topic lately, where sophisticated combinations of different technics have been employed to improve the phenotypes extraction accuracy. Even more recently released, the ChatGPT1 application nevertheless raises the question of the relevance of these approches compared to this new generic one based on an instruction-oriented LLM. In this paper, we propose a rigorous evaluation of ChatGPT and the current state-of-the-art solutions on this specific task, and discuss the possible impacts and the technical evolutions to consider in the medical domain.Clinical relevance- Deep phenotyping on electronic health records has proven its ability to improve genetic diagnosis by clinical exomes [10]. Thus, comparing state-of-the-art solutions in order to derive insights and improving research paths is essential.
Clinfo.ai: An Open-Source Retrieval-Augmented Large Language Model System for Answering Medical Questions using Scientific Literature

The quickly-expanding nature of published medical literature makes it challenging for clinicians and researchers to keep up with and summarize recent, relevant findings in a timely manner. While several closed-source summarization tools based on large language models (LLMs) now exist, rigorous and systematic evaluations of their outputs are lacking. Furthermore, there is a paucity of high-quality datasets and appropriate benchmark tasks with which to evaluate these tools. We address these issues with four contributions: we release Clinfo.ai, an open-source WebApp that answers clinical questions based on dynamically retrieved scientific literature; we specify an information retrieval and abstractive summarization task to evaluate the performance of such retrieval-augmented LLM systems; we release a dataset of 200 questions and corresponding answers derived from published systematic reviews, which we name PubMed Retrieval and Synthesis (PubMedRS-200); and report benchmark results for Clinfo.ai and other publicly available OpenQA systems on PubMedRS-200.
Validity and reliability of artificial intelligence chatbots as public sources of information on endodontics

Abstract Aim This study aimed to evaluate and compare the validity and reliability of responses provided by GPT‐3.5, Google Bard, and Bing to frequently asked questions (FAQs) in the field of endodontics. Methodology FAQs were formulated by expert endodontists ( n = 10) and collected through GPT‐3.5 queries ( n = 10), with every question posed to each chatbot three times. Responses ( N = 180) were independently evaluated by two board‐certified endodontists using a modified Global Quality Score (GQS) on a 5‐point Likert scale (5: strongly agree; 4: agree; 3: neutral; 2: disagree; 1: strongly disagree). Disagreements on scoring were resolved through evidence‐based discussions. The validity of responses was analysed by categorizing scores into valid or invalid at two thresholds: The low threshold was set at score ≥4 for all three responses whilst the high threshold was set at score 5 for all three responses. Fisher's exact test was conducted to compare the validity of responses between chatbots. Cronbach's alpha was calculated to assess the reliability by assessing the consistency of repeated responses for each chatbot. Results All three chatbots provided answers to all questions. Using the low‐threshold validity test (GPT‐3.5: 95%; Google Bard: 85%; Bing: 75%), there was no significant difference between the platforms ( p &gt; .05). When using the high‐threshold validity test, the chatbot scores were substantially lower (GPT‐3.5: 60%; Google Bard: 15%; Bing: 15%). The validity of GPT‐3.5 responses was significantly higher than Google Bard and Bing ( p = .008). All three chatbots achieved an acceptable level of reliability (Cronbach's alpha &gt;0.7). Conclusions GPT‐3.5 provided more credible information on topics related to endodontics compared to Google Bard and Bing.
Automating the verification of smart contracts in blockchain networks for improving security*

Verification of smart contracts improves the sentiment of trust in blockchain as it guarantees their authenticity. However, verification has a cost that can affect the lifecycle flow in time-sensitive systems; as these may require shorter times in code development and smart contract deployment. This paper contributes a strategy to automate the execution of verification tests over smart contracts to extract the meaningful characteristics about the source code authenticity.
Beyond the Pixel: a Photometrically Calibrated HDR Dataset for Luminance and Color Prediction

Light plays an important role in human well-being. However, most computer vision tasks treat pixels without considering their relationship to physical luminance. To address this shortcoming, we introduce the Laval Photometric Indoor HDR Dataset, the first large-scale photometrically calibrated dataset of high dynamic range 360° panoramas. Our key contribution is the calibration of an existing, uncalibrated HDR Dataset. We do so by accurately capturing RAW bracketed exposures simultaneously with a professional photometric measurement device (chroma meter) for multiple scenes across a variety of lighting conditions. Using the resulting measurements, we establish the calibration coefficients to be applied to the HDR images. The resulting dataset is a rich representation of indoor scenes which displays a wide range of illuminance and color, and varied types of light sources. We exploit the dataset to introduce three novel tasks, where: per-pixel luminance, per-pixel color and planar illuminance can be predicted from a single input image. Finally, we also capture another smaller photometric dataset with a commercial 360° camera, to experiment on generalization across cameras. We are optimistic that the release of our datasets and associated code will spark interest in physically accurate light estimation within the community. Dataset and code are available at https://lvsn.github.io/beyondthepixel/.
Online Formative Assessment in Higher STEM Education; A Systematic Literature Review

Online formative assessment in higher education Science Technology Engineering and Mathematics (STEM) is gaining attention in recent years based on the increasing use of online learning. This study, aimed at critically reviewing studies on online formative assessment in Higher education STEM between 2011 to 2020. The study adopted a qualitative research design to analyze studies based on specific themes. 1402 articles were systematically searched through Scopus, web of science, and google scholar databases. However, only 12 studies were included in this study after satisfying the inclusion criteria. All data analyses were done using the Endnote X9 reference manager. The findings reveal that, evidence about online tools used, theories used in the learning and assessment process, the domain of learning outcome assessed, assessment of practical skills, and form of assessment used have been implicitly described. However, the online formative assessments have shown great potential of yielding immediate feedback. Therefore, future studies can engage in a more extensive study on areas such as online assessment tools for assessing practical skills and strategies that can enhance the effectiveness of online formative assessment which encompasses all domains of learning outcome within STEM higher education.
Developing a Secure Image Steganographic System Using TPVD Adaptive LSB Matching Revisited Algorithm for Maximizing the Embedding Rate

Steganography is the approach for hiding any secret message in a variety of multimedia carriers like images, audio or video files. Whenever we are hiding a data, it is very important to make it invisible, so that it could be protected. A number of steganographic algorithms have been proposed based on this property of a steganographic system. This paper concentrates on integrating Tri way pixel value differencing approach and LSB matching revisited. The secret data embedded in images were images, text and audio signals so far. The proposed scheme has also come with the executable file as secret data. Also, the experimentation results show that, the important properties of a steganographic system such as imperceptibility, capacity of the carrier image and also resistance against the various steganalytic tools have also been achieved with this stego-system.
Baby talk: uma fala de adulto direcionada à criança. Que criança? Que adulto?

As a researcher interested in understanding how children are socialized in the communities to which they belong, I look at language socialization studies (Ochs and Schieffelin, 1986; 2001) for a theoretical and analytical framework to pursue this goal. Departing from this framework, I discuss a practice which is widely linked, academically, to the language acquisition process – baby talk. Baby talk was defined by Ferguson (1996 [1964]) as a special register used by adults in interaction with children. This register, according to the author, makes it easier for children to access the linguistic code that characterizes the speech community to which the children belong. Studies that follow this proposal are able to reveal how simplified – syntactically, semantically and lexically – this register appears to be. However, what is perceived is that the relations between children’s linguistic competence e the use of baby talk are essentialist since they attribute the children a cognitive limitation to understand the “adult” linguistic code, taken as the “normal” one. Thus the current study proposes to relocate the use of baby talk in the light of language socialization studies so as to understand this code as part of the local practices (Wenger, 1998) and its cultural meanings (Ochs, 1986; Ochs and Schieffelin, 2001; Schieffelin and Ochs, 1986; 1996). Making use of an ethnographic approach to the analysis of verbal exchanges (Sacks, 2000; Duranti, 1997; Erickson and Shultz, 2002 [1981]; Gumperz, 1982), the domestic environments of two Brazilian families are investigated, enabling the mapping of the local practices of baby talk. It is possible, then, to think about the local construction regarding the (linguistic) competence of children and adults’ rights and duties in interaction with children. This study reveals that, although baby talk takes place in both of the families, the local understandings that surround this practice – and which make it take place – are considerably distinct. Key words: baby talk, language socialization, childhood, family, talk-in-interaction.
Tunneling Activities Detection Using Machine Learning Techniques

Tunnel establishment, like HTTPS tunnel or related ones, between a computer protected by a security gateway and a remote server located outside the protected network is the most effective way to bypass the network security policy. Indeed, a permitted protocol can be used to embed a forbidden one until the remote server. Therefore, if the resulting information flow is ciphered, security standard tools such as application level gateways (ALG), firewalls, intrusion detection system (IDS), do not detect this violation. In this paper, we describe a statistical analysis of ciphered flows that allows detection of the carried inner protocol. Regarding the deployed security policy, this technology could be added in security tools to detect forbidden protocols usages. In the defence domain, this technology could help preventing information leaks through side channels. At the end of this article, we present a tunnel detection tool architecture and the results obtained with our approach on a public database containing real data flows.
Quantifying Relative Superiority among Many Binary-valued Diagnostic Tests in the Presence of a Gold Standard

Comparison of more than two diagnostic or screening tests for prediction of presence vs. absence of a disease or condition can be com- plicated when attempting to simultaneously optimize a pair of competing criteria such as sensitivity and specificity. A technique for quantifying rel- ative superiority of a diagnostic test when a gold standard exists in this setting is described. The proposed superiority index is used to quantify and rank performance of diagnostic tests and combinations of tests. Develop- ment of a validated model containing a subset of the tests may be improved by eliminating tests having a very small value for this index. To illustrate, we present an example using a large battery of neuropsychological tests for prediction of cognitive impairment. Using the proposed index, the battery is reduced with favorable results.
Control-Flow Carrying Code

In the previous chapter, we introduce the approach to generate a more accurate CFG by making use of function signature matching, in this chapter, we will show how to implement the CFI policy securely.
Cryptography

This chapter discusses cryptography, which is an important application of number theory. The codebreaking work done at Bletchley Park in England during the Second World War is discussed, and the fundamentals of cryptography, including private and public-key cryptosystems, are discussed.
BadUSB-C: Revisiting BadUSB with Type-C

The security of the USB protocol has been paid extensive attention to because of its wide usage. Due to the trust-by-default characteristics, USB security has caused severe problems. In this work, we extended BadUSB to support the new USB Type-C features and proposed a multi-mode attack model, BadUSB-C. This obtains UI status to make attacks more precise and effective. To the best of our knowledge, BadUSB-C is the first attack model utilizing USB Type-C. To validate the usability and effectiveness, we conducted extensive experiments to simulate daily usage and summarized the private information collected. We also discussed the recommended countermeasures for our attack model, including isolated UI rendering, which may be inspiring for future research on defense methods. This paper describes the journey of discovering BadUSB-C in my CS315 Computer Security course taught in the Fall 2020 semester.
Infrared Ship Target Segmentation Based on Active Contour Model

Infrared ship image has low contrast, weak boundary and uneven gray-scale distribution, leading to difficult ship target segmentation. Chen-Vese model, a classic active contour model, does not rely on image boundary information, which can better segment images with weak or discontinuous edges. Moreover, it has certain noise resistance, but correct segmentation result is impossible for infrared images with uneven grayscales. In view of this, this paper first uses top-hat transform or bottom-hat transform to preprocess the infrared image, increase the image contrast, so that the gray value tends to be uniform in the target and the background. Then, the improved Chen-Vese model is used to test the ship target. Experimental results show that the new method can quickly and effectively detect infrared ship targets, which is superior to the Chen-Vese model in terms of curve evolution speed and noise resistance.
Introduction

Deep learning has achieved prevailing success in a wide domain of machine learning and computer vision fields. On the other hand, sparsity and low-rankness have been popular regularizations in classical machine learning. This section is intended as a brief introduction to the basics if deep learning, and then focuses on its inherent connections to the concepts of sparsity and low-rankness.
Design and Performance Analysis of Application Specific Integrated Circuit for Internet of Things Applications

This paper proposes a novel design of application specific integrated circuit (ASIC) which is capable of connecting sensor network and other electronic systems to the internet. The transfer of data between different networks and electronic systems is controlled by internet of things (IoT) platform with the help of instruction sent to ASIC. ASIC will act as serial peripheral interface (SPI) master to all connected networks and data will be transferred serially between them. The different ASIC modules are SPI module, control module, memory module and data/instruction decoder with additional modules built-in self-test (BIST) and direct memory access (DMA). The proposed ASIC will consume less power as compared to conventional microcontroller/microprocessor due to SPI feature along with DMA on ASIC for IoT applications. It is described in very high speed integrated circuit hardware description language (VHDL) at register transfer level (RTL) and simulation is done on the Vivado 2016.2.
Extraction of Protein Sequence Motif Information using Bio-Inspired Computing

Nowadays there are many people affected by the genetic disorder, hereditary diseases, etc. The protein complexes and their functions are detected, in order to find the irregularity in the gene expression. In a group of related proteins, there exist some conserved sequence patterns (motifs) either functionally or structurally similar. The main objective of this work is to find the motif information from the given protein sequence dataset. The functionalities of the proteins are ideally found from their motif information. Clustering approach is a main data mining technique. Besides the clustering approach, the biclustering is also used in many Bioinformatics related research works. The PSO K-Means clustering and biclustering approach is proposed in this work to extract the motif information. The Motif is extracted based on the structure homogeneity of the protein sequence. In this work, the clusters and biclusters are compared based on homogeneity and motif information extracted. This study shows that biclustering approach yields better result than the clustering approach.
A Decentralized Algorithm for Deep Echo State Networks

As a class of recurrent neural networks (RNNs), echo state networks (ESNs) have been studied extensively in recent years, particularly in time-series prediction and non-linear system identification. It is well-known that deep learning (DL) has been applied to ESNs and shows more outstanding performance than conventional ESNs. However, due to the large-scale dataset and complexity required to train deep learning models, the time required to train deep ESNs based on traditional centralized algorithms is highly extended. Hence, a decentralized training algorithm combining DL and ESNs is presented in this paper to aim at multiple time scales processing of temporal data. We propose this algorithm based on an optimization program called the alternating direction method of multipliers (ADMM) and decentralized average consensus (DAC) procedure. The results based on a large-scale artificial dataset prove that it has more advantages in generalization accuracy and training time than the centralized experimental scheme.