Reviewerly/exHarmony huggingface dataset:
Remember what you did so you know what to do next

We explore using a moderately sized large language model (GPT-J 6B parameters) to create a plan for a simulated robot to achieve 30 classes of goals in ScienceWorld, a text game simulator for elementary science experiments. Previously published empirical work claimed that large language models (LLMs) are a poor fit (Wang et al., 2022) compared to reinforcement learning. Using the Markov assumption (a single previous step), the LLM outperforms the reinforcement learning-based approach by a factor of 1.4. When we fill the LLM's input buffer with as many prior steps as possible, improvement rises to 3.5x. Even when training on only 6.5% of the training data, we observe a 2.2x improvement over the reinforcement-learning-based approach. Our experiments show that performance varies widely across the 30 classes of actions, indicating that averaging over tasks can hide significant performance issues. In work contemporaneous with ours, Lin et al. (2023) demonstrated a two-part approach (SwiftSage) that uses a small LLM (T5-large) complemented by OpenAI's massive LLMs to achieve outstanding results in ScienceWorld. Our 6-B parameter, single-stage GPT-J matches the performance of SwiftSage's two-stage architecture when it incorporates GPT-3.5 turbo which has 29-times more parameters than GPT-J.
Blockchain Characteristics and Implications: Assessing the Impact on Sustainability in Turkish Sectors

Technological advances are accelerating the activation of the saving, preserving and using information in various economic models. Blockchain technology is one of the latest technologies used by many international institutions and organizations. Compared to other banking or technological technologies, it transmits and accumulates information transparently, instantly, and reliably at a low cost. As Turkey plays a significant economic role in the Middle East and Europe regions, this article highlights a sample of Turkish companies that use blockchain technology of the version 4.0. It is an attempt to measure the variables of the coefficient of trust, immutability, transparency, and cost reduction in combination with blockchain applications by scientists, administrative authors, and blockchain technologists. A quantitative descriptive approach has been adopted to examine the interrelation between blockchain applications and increase in transparency, confidence, stability, and cost reduction, based on the SPSS statistical software to produce accurate results. 19 Turkish companies were selected from 62 Turkish companies that have adopted blockchain, according to the official agency «BTCR» that tracks these organizations in Turkey. The selected companies, operating in banking, telecommunications, industry, and non-governmental associations, make large contributions to Turkey’s GDP. The study found that all four variables in the sectors improved significantly after the introduction of blockchain technology, but the systems for monitoring and tracking their influence were complex ones.
Generic Construction of Fully Anonymous Broadcast Authenticated Encryption with Keyword Search with Adaptive Corruptions

As a multireceiver variant of public key authenticated encryption with keyword search (PAEKS), broadcast authenticated encryption with keyword search (BAEKS) was proposed by Liu et al. (ACISP 2021). BAEKS focuses on receiver anonymity, where no information about the receiver is leaked from ciphertexts, which is reminiscent of the anonymous broadcast encryption. Here, there are rooms for improving their security definitions, e.g., two challenge sets of receivers are selected before the setup phase, and an adversary is not allowed to corrupt any receiver. In this paper, we propose a generic construction of BAEKS derived from PAEKS that provides ciphertext anonymity and consistency in a multireceiver setting. The proposed construction is an extension of the generic construction proposed by Libert et al. (PKC 2012) for the fully anonymous broadcast encryption and provides adaptive corruptions. We also demonstrate that the Qin et al. PAEKS scheme (ProvSec 2021) provides ciphertext anonymity and consistency in a multireceiver setting and can be employed as a building block of the proposed generic construction.
The Development of Metabolic Syndromes Learning Application Using Augmented-Virtual Reality Technology

Due to the current era's technology, work, competition, and fast-paced lifestyle, the Thai population lacks exercise and proper healthcare, especially in terms of dietary habits. Metabolic syndrome has become a significant health issue at present, with the number of patients suffering from this condition continuously increasing each year. Raising awareness about the dangers of metabolic syndrome may help solve this problem. Therefore, we designed and developed an augmented-virtual reality (AVR) application to promote learning about metabolic syndrome and evaluate users' satisfaction with it. The data collection process targets two groups of participants: experts in medical media technology and augmented reality (AR)/ virtual reality (VR) development as well as individuals aged between 20 and 40 years. We used a quality assessment questionnaire to collect data from the experts. The assessment included evaluating the accuracy of the media content and the quality of the AVR application. Pre- and post-test was conducted to compare knowledge levels before and after using the application among a group of users. Additionally, a user satisfaction survey was conducted to gather feedback on the media's educational content related to metabolic syndrome using AVR technology. The results demonstrate that the accuracy of the media content was rated at an excellent level, while the overall application design was rated at a good level, according to the experts. Users' satisfaction with the application was also considered good. The statistical analysis showed a significant improvement in knowledge levels after using the application, based on the pre-and post-test comparisons. In conclusion, users expressed high satisfaction with the application and gained significant knowledge about metabolic syndrome, as supported by statistical evidence.
Enhancing Editability in Permissionless Blockchain: A Three-Chain Model for Efficiency and Ledger Consistency

&lt;p&gt;Blockchain technology has gained significant attention due to its decentralized, tamper-resistant, and transparent nature. However, the inherent immutability of blockchain poses challenges for certain applications. This article presents a novel three-chain model based on the chameleon hash function to address the need for editability in blockchain systems. The model includes a dedicated pool chain that provides optimal conditions for editing operations, along with composite supervision and audit strategies to ensure compliance, security, and traceability. To maintain ledger consistency, a correction chain stores audited edited blocks, while the main chain is updated accordingly. The model’s feasibility is validated through experimentation, showing a minimal impact on the performance of the editable main chain. Contributions of this research include clear definitions of data ownership, editing permissions, and the completion flag for editing operations on the blockchain. Furthermore, it effectively resolves conflicts between decentralized editing and editing waiting, while ensuring the ledger consistency. The proposed three-chain model offers a secure and efficient solution to enhance blockchain editability, opening up new possibilities for its application in diverse domains.&lt;/p&gt;
Effect of Plasmodium Infections on CD4 Cells, Neutrophil and Lymphocytes

Knowledge of malaria-immunity is essential for understanding the pathology, treatment &amp; vaccine production. So, changes in CD4 in malaria patients were studied at a teaching hospital in Enugu, Nigeria. 45 patients on doctor’s provisional diagnosis of malaria were examined for Plasmodium infections and the degree of parasitaemia (0/+/ ++/ &amp; +++). Positive/(+) and negative/(0) samples were thereafter counted for CD4 using Flow Cytometry. CD4 cell count of Plasmodium-negative specimens were within reference ranges (RR): (464-1308); mean value: 835. Fifteen (+) parasitaemia showed lower ranges of CD4 count (502-1282); mean: 678; with immune falls in one (427). 9/12 (++) parasitaemia showed crash in CD4 counts, range: 301- 415; mean: 399. Seven (+++) parasitaemia showed crashes in CD4 counts, range: 160-357; mean: 225. As parasitaemia rises, neutrophils, Total WBC and lymphocyte significantly rise in numeric. Malaria causes immunosuppression; unlike prevailing reports, should accelerate complications in immunodeficients-patients, Haemolytic-anaemia results only from chronic cases. CD4 counts should be a paradigm in malaria investigation.
The Effect of Using Financial and Banking Application Programs via Smart Phones on Customers' Satisfaction in Jordanian Commercial Banks

The study aimed to identify the impact of using of financial and banking application programs via smart phones on customers' satisfaction in Jordanian commercial banks. The researcher depended on the analytical descriptive method for building the study. The population of the study included the customers of Jordanian commercial banks in the north region who use smart phones and financial and banking application programs through them. The researcher developed the study tool (the questionnaire) to collect information, and (500) questionnaires were distributed to the respondents, (90) of them were excluded, because the respondents do not use smart applications through phones. The researcher relied on (410) questionnaires and analyzed their data. The results of the study were: There is an impact of the applied and financial programs combined, which were represented in the following dimensions (cash transfer programs, bank statement programs, bill payment programs) on customers' satisfaction in Jordanian commercial banks. The data analysis showed that the dimension of bank statement programs gained the most interest by the respondents. Among the most important recommendations of the study were: Carrying out marketing campaigns aimed at increasing customers’ awareness of electronic banking services to acquaint customers with the advantages and benefits of the services provided through smart phones, their importance, and how to use them. Also, focusing on trust factors and providing necessary protection for phone applications because of their major role in attracting a larger segment of customers and increasing their level of satisfaction.
ANALISIS MEKANIK DAN TERMAL MATERIAL KOMPOSIT BERBASIS SERAT ECENG GONDOK SEBAGAI BAHAN KEMASAN RAMAH LINGKUNGAN

This study aims to analyze the mechanical and thermal properties of water hyacinth fiber-based composite materials as an alternative to environmentally friendly packaging materials. This composite material is produced by mixing water hyacinth fiber with a natural polymer matrix that can decompose easily. Manufacturing methods include mixing, pressing, and thermal treatment processes. The mechanical properties of composite materials, including tensile strength and resistance to deformation, are tested using tensile tests and hardness tests. Thermal analysis is carried out through thermogravimetric tests (TGA) to identify the thermal stability and decomposition of the material. The results showed that the water hyacinth fiber-based composite materials had fairly good mechanical properties in certain mixture compositions, although they were not as strong as composite materials using synthetic fibers. However, this material shows potential in packaging applications that do not require extreme mechanical strength. The mechanical properties of the water hyacinth fiber composite produced lead to the fulfillment of the need for use as packaging, which is light, flexible and not easily broken. The thermal properties of the composites show faster decomposition compared to synthetic fiber based composites, but are still within the acceptable range for single-use packaging. With its environmentally friendly properties, this composite material can be an attractive alternative to reduce the use of plastic in the packaging industry. Nonetheless, improvements in manufacturing processes and material formulations are still required to improve their mechanical and thermal properties and maintain packaging durability under diverse usage conditions.
Optimizing quantum noise-induced reservoir computing for nonlinear and chaotic time series prediction

Abstract Quantum reservoir computing is strongly emerging for sequential and time series data prediction in quantum machine learning. We make advancements to the quantum noise-induced reservoir, in which reservoir noise is used as a resource to generate expressive, nonlinear signals that are efficiently learned with a single linear output layer. We address the need for quantum reservoir tuning with a novel and generally applicable approach to quantum circuit parameterization, in which tunable noise models are programmed to the quantum reservoir circuit to be fully controlled for effective optimization. Our systematic approach also involves reductions in quantum reservoir circuits in the number of qubits and entanglement scheme complexity. We show that with only a single noise model and small memory capacities, excellent simulation results were obtained on nonlinear benchmarks that include the Mackey-Glass system for 100 steps ahead in the challenging chaotic regime.
Formation of creative thinking in music lessons

At present, when teaching at various educational levels, a lot of attention is paid to the need for the formation of creative thinking in teaching. Creative thinking is necessary for a modern person in order to be successful in professional activities, especially for those professions that are associated with creativity. Over the past 25 years, music educators have witnessed an increased focus on student-centered approaches to teaching and learning, in which musical growth occurs through creative experiences. Our profession as music educators' interest in constructivist ideas about how learning happens is displacing the teacher as the sole bearer of official knowledge and increasing student control over musical decision making in the classroom. In connection with the foregoing, the author of this article made an attempt to scientific analysis and critical understanding of the problem of the formation of creative thinking in music lessons in a modern school.
Framing of Police Enforcement of the Covid-19 Laws in Kenya: A Comparative Analysis of the Daily Nation and Standard Newspapers

This study analysed how the Standard and The Daily Nation Newspapers framed the Kenyan police enforcement of Legal Notice No. 36 of 2020. Three major objectives were interrogated; the frames used by the two newspapers in reporting police enforcement of the curfew, the figures of speech used in the framing the police enforcement work and the sources of the stories used. Content analysis was the main data collection method. The key findings of the study were: The Daily Nation and the Standard newspapers framed the police enforcement of the curfew through negative valance via use of: brutality frames, conflict frames, death and murder frames. It also emerged that, the voicing of the police enforcement of the curfew order was largely supressed and left mainly to the mass media institutions and the journalists. Inferentially, the two newspapers played their watchdog role effectively. It also emerged that, critical voices necessary in ensuring accountability and transparent of the police enforcement of Covid 19 curfew in Kenya like: The Judiciary, Parliament, International Community and other key human right agencies were silent. It is recommended that, police should be trained on matters human rights and freedom and that both the journalists and the public should be empowered in order to hold police to account for their sometimes illegal actions during executions of their mandates. Keywords: Media framing, Police brutality, Governance, public order, DOI: 10.7176/NMMC/105-01 Publication date: October 31 st 2023
CACNN: Capsule Attention Convolutional Neural Networks for 3D Object Recognition

Recently, view-based approaches, which recognize a 3D object through its projected 2-D images, have been extensively studied and have achieved considerable success in 3D object recognition. Nevertheless, most of them use a pooling operation to aggregate viewwise features, which usually leads to the visual information loss. To tackle this problem, we propose a novel layer called capsule attention layer (CAL) by using attention mechanism to fuse the features expressed by capsules. In detail, instead of dynamic routing algorithm, we use an attention module to transmit information from the lower level capsules to higher level capsules, which obviously improves the speed of capsule networks. In particular, the view pooling layer of multiview convolutional neural network (MVCNN) becomes a special case of our CAL when the trainable weights are chosen on some certain values. Furthermore, based on CAL, we propose a capsule attention convolutional neural network (CACNN) for 3D object recognition. Extensive experimental results on three benchmark datasets demonstrate the efficiency of our CACNN and show that it outperforms many state-of-the-art methods.
Beyond the Surface of Digital Contact Tracing: Delving into the Interconnected World of Technology, Individuals, and Society

&lt;p&gt;Digital Contact Tracing (DCT) is a great example of information systems assisting societal problems. However, privacy concerns lead to reduced DCT adoption rates. When dealing with critical societal issues, policymakers seek to use various strategies, such as interventions and financial subsidies, to steer the behavors of individuals. This causes individuals to face a sophisticated decision-making process when coping with the public health crisis and the adoption of DCT, i.e., giving up privacy and freedom to gain information to remain healthy. In this paper, we consider a scenario, where policymakers allocate rewards to individuals to motivate their compliance with the interventions; And the individuals decide the optimal compliance effort based on their health state, privacy loss, interaction with their neighbors, and rewards. To tackle the trade-off between a number of individuals and policymakers, in this paper, we propose a Leader-Followers Mean-Field Game model to analyze this time-dependent, dynamic, and large-scale decision-making problem. The numerical results demonstrate that by allocating appropriate rewards, policymakers can play a role in guiding the behavior of individuals in various scenarios.&lt;/p&gt;
Regularized Linear Regression for Binary Classification

Regularized linear regression is a promising approach for binary classification problems in which the training set has noisy labels since the regularization term can help to avoid interpolating the mislabeled data points. In this paper we provide a systematic study of the effects of the regularization strength on the performance of linear classifiers that are trained to solve binary classification problems by minimizing a regularized least-squares objective. We consider the over-parametrized regime and assume that the classes are generated from a Gaussian Mixture Model (GMM) where a fraction $c<\frac{1}{2}$ of the training data is mislabeled. Under these assumptions, we rigorously analyze the classification errors resulting from the application of ridge, $\ell_1$, and $\ell_\infty$ regression. In particular, we demonstrate that ridge regression invariably improves the classification error. We prove that $\ell_1$ regularization induces sparsity and observe that in many cases one can sparsify the solution by up to two orders of magnitude without any considerable loss of performance, even though the GMM has no underlying sparsity structure. For $\ell_\infty$ regularization we show that, for large enough regularization strength, the optimal weights concentrate around two values of opposite sign. We observe that in many cases the corresponding "compression" of each weight to a single bit leads to very little loss in performance. These latter observations can have significant practical ramifications.
LLMs-augmented Contextual Bandit

Contextual bandits have emerged as a cornerstone in reinforcement learning, enabling systems to make decisions with partial feedback. However, as contexts grow in complexity, traditional bandit algorithms can face challenges in adequately capturing and utilizing such contexts. In this paper, we propose a novel integration of large language models (LLMs) with the contextual bandit framework. By leveraging LLMs as an encoder, we enrich the representation of the context, providing the bandit with a denser and more informative view. Preliminary results on synthetic datasets demonstrate the potential of this approach, showing notable improvements in cumulative rewards and reductions in regret compared to traditional bandit algorithms. This integration not only showcases the capabilities of LLMs in reinforcement learning but also opens the door to a new era of contextually-aware decision systems.
MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning

Code LLMs have emerged as a specialized research field, with remarkable studies dedicated to enhancing model's coding capabilities through fine-tuning on pre-trained models. Previous fine-tuning approaches were typically tailored to specific downstream tasks or scenarios, which meant separate fine-tuning for each task, requiring extensive training resources and posing challenges in terms of deployment and maintenance. Furthermore, these approaches failed to leverage the inherent interconnectedness among different code-related tasks. To overcome these limitations, we present a multi-task fine-tuning framework, MFTcoder, that enables simultaneous and parallel fine-tuning on multiple tasks. By incorporating various loss functions, we effectively address common challenges in multi-task learning, such as data imbalance, varying difficulty levels, and inconsistent convergence speeds. Extensive experiments have conclusively demonstrated that our multi-task fine-tuning approach outperforms both individual fine-tuning on single tasks and fine-tuning on a mixed ensemble of tasks. Moreover, MFTcoder offers efficient training capabilities, including efficient data tokenization modes and PEFT fine-tuning, resulting in significantly improved speed compared to traditional fine-tuning methods. MFTcoder seamlessly integrates with several mainstream open-source LLMs, such as CodeLLama and Qwen. Leveraging the CodeLLama foundation, our MFTcoder fine-tuned model, \textsc{CodeFuse-CodeLLama-34B}, achieves an impressive pass@1 score of 74.4\% on the HumaneEval benchmark, surpassing GPT-4 performance (67\%, zero-shot). MFTCoder is open-sourced at \url{https://github.com/codefuse-ai/MFTCOder}
Monitoring Inactivity of Single Older Adults at Home

A new application for real-time monitoring of the lack of movement in older adults' own homes is proposed, aiming to support people's lives and independence in their later years. A lightweight camera monitoring system, based on an RGB-D camera and a compact computer processor, was developed and piloted in community homes to observe the daily behavior of older adults. Instances of body inactivity were detected in everyday scenarios anonymously and unobtrusively. These events can be explained at a higher level, such as a loss of consciousness or physiological deterioration. The accuracy of the inactivity monitoring system is assessed, and statistics of inactivity events related to the daily behavior of the older adults are provided. The results demonstrate that our method performs accurately in inactivity detection across various environments, including low room lighting, TV flickering, and different camera views.