Reviewerly/exHarmony huggingface dataset:
Indexing AI Risks with Incidents, Issues, and Variants

Two years after publicly launching the AI Incident Database (AIID) as a collection of harms or near harms produced by AI in the world, a backlog of "issues" that do not meet its incident ingestion criteria have accumulated in its review queue. Despite not passing the database's current criteria for incidents, these issues advance human understanding of where AI presents the potential for harm. Similar to databases in aviation and computer security, the AIID proposes to adopt a two-tiered system for indexing AI incidents (i.e., a harm or near harm event) and issues (i.e., a risk of a harm event). Further, as some machine learning-based systems will sometimes produce a large number of incidents, the notion of an incident "variant" is introduced. These proposed changes mark the transition of the AIID to a new version in response to lessons learned from editing 2,000+ incident reports and additional reports that fall under the new category of "issue."
SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

Large language models (LLMs) show excellent performance but are compute- and memory-intensive. Quantization can reduce memory and accelerate inference. However, existing methods cannot maintain accuracy and hardware efficiency at the same time. We propose SmoothQuant, a training-free, accuracy-preserving, and general-purpose post-training quantization (PTQ) solution to enable 8-bit weight, 8-bit activation (W8A8) quantization for LLMs. Based on the fact that weights are easy to quantize while activations are not, SmoothQuant smooths the activation outliers by offline migrating the quantization difficulty from activations to weights with a mathematically equivalent transformation. SmoothQuant enables an INT8 quantization of both weights and activations for all the matrix multiplications in LLMs, including OPT, BLOOM, GLM, MT-NLG, Llama-1/2, Falcon, Mistral, and Mixtral models. We demonstrate up to 1.56x speedup and 2x memory reduction for LLMs with negligible loss in accuracy. SmoothQuant enables serving 530B LLM within a single node. Our work offers a turn-key solution that reduces hardware costs and democratizes LLMs. Code is available at https://github.com/mit-han-lab/smoothquant.
KRiPTO PARA VE PARANIN FONKSİYONLARI

It goes without saying that cryptocurrency is a technologically advanced type of electronic currency that came into our lives in 2009, but still not up to scratch. When you take into account particularly the last two decades of the course, its place in money markets is subject to critical evaluations, while the awareness of cryptocurrency becomes ubiquitous. One of the main intriguing points to be dug out at this point is the extent to which cryptocurrency meet the functions of money. In the study, how extent the cryptocurrency types meet the functions of money is sought to dig out. Besides, the operability of the functions of money in the cryptocurrency is pinpointed with a multifaceted evaluation. Cryptocurrency and blockchain technology, which are clearly summarized with the aforementioned approach, are considered within the scope of today's usage areas. Within the scope of study, which also includes the advantages of cryptocurrency and blockchain technology, the possible usage areas and possible developments of cryptocurrencies in the following period are also highlighted, and their relationship with the functions of money is established.
Modelling of a Large Solar PV Facility: England’s Mallard Solar Farm Case Study

With reference to energy generation, the global society has to urgently address three factors that are now critical: sustainability in terms of climate change, security in terms of the war that is currently raging in Europe with consequences that are being felt around the globe and the steep incline of fossil-fuel based energy costs. Around the world, large-scale solar farms are being constructed with tracking systems to improve the efficiency of photovoltaic (PV) modules. This article presents a comparison of energy generation of fixed-slope versus tracking PV modules. The analysis was based on a twenty-year dataset for two locations, namely, Lincoln (England) and Bhavnagar (India), which differ in terms of latitude, sky clarity and ambient temperature. It was demonstrated that a fixed-slope system facing the equator provides a healthy energy receipt that is a high fraction of the energy receipt of a tracking system. Furthermore, analysis was also carried out for a PV facility that will host the largest solar farm in England to conclude that regardless of the solar farm installation location, the use of bifacial PV is beneficial.
An Analysis of English Textbook for Tenth Graders of Vocational High School in Indonesia

This study aims to investigate (1) material completeness, includes a short interpersonal text, transactional text and functional text, (2) material depth, includes the exposure, retention and production, and 3) the life skill development, includes personal skills, social skills, academic skills and vocational skills. Within the scope of the current study, it was analyzed an English textbook used by the tenth graders of Vocational High School. The research method used in this study was descriptive qualitative following four systematic steps, namely deciding on the subject, referring the criteria of English textbook analysis, applying the criteria by using checklist and interview, and analyzing the textbook. The data were gathering in this research through the checklist and interview containing the criteria of textbook analysis. Checklist used to gain the early data then interview used to gain the data in depth from the participant. The data were analyzing in this research through impressionistic overview and depth evaluation. As expected, the current study found that that “Bahasa Inggris” textbook has made the criteria of a good textbook in term of material completeness, which is fulfill a short interpersonal text, transactional text and functional text. In the material depth, it fulfills the exposure, retention and production. Whereas in life skill development, the textbook provides the personal skills, social skills, academic skills and does not fulfill the vocational skill.
Beliefs of Elementary School Teachers and Secondary School Mathematics Teachers During the Teaching Process

Background: Teacher practices when teaching mathematics are supported by their beliefs on how this subject should be approached. This has an impact on the learning of students who generally perceive mathematics as an incomprehensible mechanical activity in which a series of algorithms must be followed to obtain a solution. Therefore, the following questions arise: What are the beliefs of mathematics teachers regarding the teaching process? Which methodologies do they use in the classroom? Objective: The objective was to analyse the beliefs of elementary school teachers and secondary school mathematics teachers during the teaching process. Design: The study used a descriptive quantitative approach. Setting and participants: The sample consisted of 100 elementary school teachers and secondary school mathematics teachers from three regions in Chile. Data collection and analysis: Due to the Covid-19 health crisis, Google Drive was used to send a Likert questionnaire to each of the study participants. The information was processed and quantitatively analysed with the SPPS 25.0 statistical software. Results: Statistically significant differences were found based on specialisation, place of work, school administration, and gender of the participants. Conclusions: Teachers considered that knowing, learning, and applying mathematical knowledge was possible through rote and routine activities. In addition, teachers with 5 to 8 years of experience believed that mathematics develops problem-solving skills.
QACL: Quartet attention aware closed-loop learning for abdominal MR-to-CT synthesis via simultaneous registration

Synthesis of computed tomography (CT) images from magnetic resonance (MR) images is an important task to overcome the lack of electron density information in MR-only radiotherapy treatment planning (RTP). Some innovative methods have been proposed for abdominal MR-to-CT synthesis. However, it is still challenging due to the large misalignment between preprocessed abdominal MR and CT images and the insufficient feature information learned by models. Although several studies have used the MR-to-CT synthesis to alleviate the difficulty of multi-modal registration, this misalignment remains unsolved when training the MR-to-CT synthesis model. In this paper, we propose an end-to-end quartet attention aware closed-loop learning (QACL) framework for MR-to-CT synthesis via simultaneous registration. Specifically, the proposed quartet attention generator and mono-modal registration network form a closed-loop to improve the performance of MR-to-CT synthesis via simultaneous registration. In particular, a quartet-attention mechanism is developed to enlarge the receptive fields in networks to extract the long-range and cross-dimension spatial dependencies. Experimental results on two independent abdominal datasets demonstrate that our QACL achieves impressive results with MAE of 55.30±10.59 HU, PSNR of 22.85±1.43 dB, and SSIM of 0.83±0.04 for synthesis, and with Dice of 0.799±0.129 for registration. The proposed QACL outperforms the state-of-the-art MR-to-CT synthesis and multi-modal registration methods.
Researching interactions between humans and machines: methodological challenges

Abstract Communication scholars are increasingly concerned with interactions between humans and communicative agents. These agents, however, are considerably different from digital or social media: They are designed and perceived as life-like communication partners (i.e., as “communicative subjects”), which in turn poses distinct challenges for their empirical study. Hence, in this paper, we document, discuss, and evaluate potentials and pitfalls that typically arise for communication scholars when investigating simulated or non-simulated interactions between humans and chatbots, voice assistants, or social robots. In this paper, we focus on experiments (including pre-recorded stimuli, vignettes and the “Wizard of Oz”-technique) and field studies. Overall, this paper aims to provide guidance and support for communication scholars who want to empirically study human-machine communication. To this end, we not only compile potential challenges, but also recommend specific strategies and approaches. In addition, our reflections on current methodological challenges serve as a starting point for discussions in communication science on how meaning-making between humans and machines can be investigated in the best way possible, as illustrated in the concluding section.
An Improved Chicken Swarm Optimization Algorithm for Solving Multimodal Optimization Problems

To solve the premature convergence problem of the standard chicken swarm optimization (CSO) algorithm in dealing with multimodal optimization problems, an improved chicken swarm optimization (ICSO) algorithm is proposed by referring to the ideas of bacterial foraging algorithm (BFA) and particle swarm optimization (PSO) algorithm. First, in order to improve the depth search ability of the algorithm, considering that the chicks have the weakest optimization ability in the whole chicken swarm, the replication operation of BFA is introduced. In the role update process of the chicken swarm, the chicks are replaced by the same number of chickens with the strongest optimization ability. Then, to maintain the population diversity, the elimination-dispersal operation is introduced to disperse the chicks that have performed the replication operation to any position in the search space according to a certain probability. Finally, the PSO algorithm is integrated to improve the global optimization ability of the algorithm. The experimental results on the CEC2014 benchmark function test suite show that the proposed algorithm has good performance in most test functions, and its optimization accuracy and convergence performance are also better than BFA, artificial fish swarm algorithm (AFSA), genetic algorithm (GA), and PSO algorithm, etc. In addition, the ICSO is also utilized to solve the welded beam design problem, and the experimental results indicate that the proposed algorithm has obvious advantages over other comparison algorithms. Its disadvantage is that it is not suitable for dealing with large-scale optimization problems.
Performance Prediction of Building Integrated Photovoltaic System Using Hybrid Deep Learning Algorithm

In a grid-connected photovoltaic system, forecasting is a necessary and critical step. Solar Power is very nonlinear; this article develops and analyses building integrated photovoltaic (BIPV) forecasting algorithms for different timeframes, such as an hour, a day, and a week ahead, to manage grid operation effectively. However, a model built for a certain time scale may improve performance at that time scale but cannot be utilized to make predictions at other time scales. Here, we demonstrate how to use the multitask learning algorithm to create a multitime scale model for solar BIPV forecasting. Effective resource distribution across several tasks is shown. The suggested multitask learning approach is implemented using LSTM neural networks and evaluated over a range of horizons. We employed a modified version of the Chicken Swarm Optimizer (CSO) that takes the best features of the CSO and the GWO algorithms and merges them into one efficient approach to estimate the hyperparameters of the proposed LSTM model. The proposed approach consistently outperformed state-of-the-art single-timescale forecasting algorithms across all time scales.
Chinese technical terminology extraction based on DC-value and information entropy

China's technology is developing rapidly, and the number of patent applications has surged. Therefore, there is an urgent need for technical managers and researchers that how to apply computer technology to conduct in-depth mining and analysis of lots of Chinese patent documents to efficiently use patent information, perform technological innovation and avoid R&D risks. Automatic term extraction is the basis of patent mining and analysis, but many existing approaches focus on extracting domain terms in English, which are difficult to extend to Chinese due to the distinctions between Chinese and English languages. At the same time, some common Chinese technical terminology extraction methods focus on the high-frequency characteristics, while technical domain correlation characteristic and the unithood feature of terminology are given less attention. Aiming at these problems, this paper proposes a Chinese technical terminology method based on DC-value and information entropy to achieve automatic extraction of technical terminology in Chinese patents. The empirical results show that the presented algorithm can effectively extract the technical terminology in Chinese patent literatures and has a better performance than the C-value method, the log-likelihood ratio method and the mutual information method, which has theoretical significance and practical application value.
Model elasticity for hardware heterogeneity in federated learning systems

Most Federated Learning (FL) algorithms proposed to date obtain the global model by aggregating multiple local models that typically share the same architecture, thus overlooking the impact on the hardware heterogeneity of edge devices. To address this issue, we propose a model-architecture co-design framework for FL optimization based on the new concept of model elasticity. More precisely, we enable local devices to train different models belonging to the same architecture family, selected to match the resource budgets (e.g., latency, memory, power) of various edge devices. Our results on EMNIST and CIFAR-10 for both IID and non-IID cases show up to 2.44X less data transferred per communication round and up to 100X reduction in the number of communication rounds, while providing the same or better accuracy compared to existing approaches.
Mobil Destekli Okumanın İngilizce Öğrenenlerin Kelime Bilgisi Gelişimi Üzerindeki Etkilerinin Araştırılması

Abstract. In recent years, the proliferation of mobile technologies is transforming educational practices around the world. In this regard, students are attending classes with their mobile or tablet devices, on which they do most of their reading and writing activities. This has resulted in new understanding of literacy as being multimodal, fluid, sociocultural, and dynamic social practices. Meanwhile, mobile-assisted language learning developed exponentially, and its implementation has been reported to be generally effective in language education compared to traditional materials. Within this line of research, the current study examined the impacts of mobile assisted reading on English language learners’ vocabulary development. The participants of the research were 52 adult language learners divided into one experimental and one control groups. The participants in the experimental group employed mobile devices for reading a number of selected and simplified news reports, however, the control group used traditional print-based materials for reading the same content over the course of 10 weeks. The participants’ vocabulary gains were compared in pre- and post-tests and the findings indicated that the two groups improved their vocabulary knowledge, but the experimental group learned more vocabulary items and outperformed the control group on post-test. The findings highlighted the significant contribution of mobile phones in improving second language reading with associated impacts on vocabulary development. The study has implications for language teachers and materials development in teaching English to speakers of other languages.
An efficient Privacy-Preserving Recommender System

The popularity of online recommender systems has soared. They are deployed on numerous websites and gather tremendous amounts of user data that are necessary for recommendation purposes. However, this data may pose a severe threat to user privacy, if accessed by untrusted parties or used inappropriately. The goal of a privacy-preserving recommender system is to hide user ratings from the system and yet allow them to make recommendations. A recent example is the privacy-preserving recommender scheme proposed by Pranav Verma et al. Their scheme can ensure the privacy of user ratings against a malicious server as shown by Mu, Shao, and Miglani. However, this scheme still requires quite high communication and computation costs. This paper proposes an improved protocol using Secure multi-party computation. The theoretical and experimental analysis shows that the proposed method is effective in both computing and communication compared to other method. Moreover, the new protocol preserves the privacy of the honest users against the miner and up to n-2 corrupted users.
D-TransT: Deformable Transformer Tracking

The tracker based on the Siamese network describes the object-tracking task as a similarity-matching problem. The Siamese network is the current mainstream model. It achieves similarity learning by applying correlation filters to the target and search branches’ convolution features. However, because the correlation operation uses a local linear matching process, semantic information is lost, and it is simple to run into the issue of local optimality. Transformer Tracking has recently been proposed using an attention-based feature fusion network instead of the previous correlation operation to achieve excellent results. However, it only uses limited feature space resolution. Because of the limitations of the Transformer module, the network’s convergence is also very slow. We propose Deformable Transformer Tracking (D-TransT) which employs a deformable attention module that pre-filters for prominent key elements among all feature map pixels using a small set of sampling locations, and this module can be naturally extensible to aggregating multi-scale features. D-TransT can have faster convergence and better prediction than Transformer Tracking. D-TransT improves the convergence speed by 29.4% and achieves 65.6%, 73.3%, and 69.1% in AUC, PNorm and P, respectively. The experimental results demonstrate that the proposed tracker performs better than the most state-of-the-art algorithm.
LBFT: An Asynchronous Committee-Based Blockchain Storage Strategy on Zero Trust Model

The high storage costs brought by the full-replication storage strategy adopted in most existing blockchain systems have become the main bottleneck to system scalability. To address the above, we propose an asynchronous committee-based blockchain storage strategy named lightweight BFT (LBFT), which can be applied to more diverse scenarios with better system performance. It is the first blockchain storage scheme that is designed on the conception of the zero-trust model, achieving higher-level security and fending off internal, as well as external attackers. In addition, it makes the following progress on system performance on the premise of maintaining the merits of the blockchain: (1) decreases communication complexity by involving only a part of the nodes in each decoding round; (2) enhances the robustness of the scheme regardless of the time assumption of the network; (3) improves the computational efficiency in the encoding and decoding process; and (4) reduces the storage costs and improves system scalability. In addition, we implemented experiments on LBFT and two other existing blockchain-based storage strategies, and the experimental results showed that LBFT indeed has significant improvements in system performance.
Plataforma Virtual y rendimiento académico de los estudiantes durante la pandemia: Una revisión sistemática

The article presented is a systematic review to be able to answer questions such as: What are the reflections onthe implementation of virtual platforms during the COVID-19 pandemic? and What role do virtual platforms play instudent academic performance?, with the objective: to determine the role of virtual platforms in student academicperformance during the pandemic. Material and method: this study is a systematic review of 16 articles that presentsa qualitative approach, with a non-experimental, bibliographic methodology. To collect the information, the searchprocess was carried out in two phases for data coding, the PRISMA flow diagram was performed. Conclusions:Virtual platforms must be created according to the student’s needs to facilitate the teaching and learning processwith an inclusive approach. 34% of students resist change since the virtual environment is colder, adapting to thisnew form of learning being slower. 81% of students affirm that there is frequently a bad internet signal, making theteaching and learning process difficult.
Decolonization should extend to collaborations, authorship and co-creation of knowledge

Institutions must change the reward structure to value a wider range of perspectives. Institutions must change the reward structure to value a wider range of perspectives.