Miracle-dz/UniFAR-FTUs huggingface dataset:
Chapter 40 Intelligence , Race , and Psycholo gical Testing

The Stanford Encyclopedia of Philosophy has an entry for “Logic and Artificial Intelligence” (Thomason 2014) and “Virtue Epistemology” (Turri and Alfano 2011), but no entry for “Intelligence. ” Likewise, www.philpapers. org, the largest curated database of philosophical publications in the Anglophone world, has entries for “Philosophy of Artificial Intelligence,” “Special Topics in Artificial Intelligence,” “Philosophy of Artificial Intelligence, Miscellaneous,” “The Nature of Artificial Intelligence,” “Artificial Intelligence Methodology,” “Extraterrestrial Life and Intelligence,” and “Ethics of Artificial Intelligence,” but nothing for “Intelligence,” “Human Intelligence,” or “Intelligence and Race. ” As of the writing of this chapter, there were just three entries in the category “Race and IQ”: Alfano (2014), Block (1996), and Kaplan and Grønfeldt Winther (2014). A contribution on the philosophy of race and intelligence is long overdue. That said, there is an immense amount we cannot address in this chapter. Among other things, we will discuss only superficially the ontology of race, the use of intelligence testing to justify oppression and the potential implications of this, and finally how one might mitigate the outcomes associated with racial group stigmatization in this context. This chapter has two main goals: to update philosophers on the state of the art in the scientific psychology of intelligence, and to explore more recent and relevant theoretical phenomena surrounding the measurement invariance of intelligence tests. First, we provide a brief history of the scientific psychology of intelligence. Next, we discuss the metaphysics of intelligence in light of scientific studies in psychology and neuroimaging. Finally, we turn to recent skeptical developments related to measurement invariance. These have largely focused on attributability: Where do the mechanisms and dispositions that explain people’s performance on tests of intelligence inhere— in the agent, in the local testing environment, in the culture, or in the interactions among these? After explaining what measurement invariance is in the context of intelligence testing, we will discuss stereotype threat as evidence challenging measurement invariance views of intelligence. In conclusion, we will review recent psychological theories that provide ways for combatting the pernicious and stigmatizing effects of this phenomenon. OUP UNCORRECTED PROOF – REVISES, Sat Sep 1
Chemical Identification and Indexing in PubMed Articles via BERT and Text-to-Text Approaches

The Biocreative VII Track-2 challenge consists of named entity recognition, entity-linking (or entity-normalization), and topic indexing tasks -- with entities and topics limited to chemicals for this challenge. Named entity recognition is a well-established problem and we achieve our best performance with BERT-based BioMegatron models. We extend our BERT-based approach to the entity linking task. After the second stage of pretraining BioBERT with a metric-learning loss strategy called self-alignment pretraining (SAP), we link entities based on the cosine similarity between their SAP-BioBERT word embeddings. Despite the success of our named entity recognition experiments, we find the chemical indexing task generally more challenging. In addition to conventional NER methods, we attempt both named entity recognition and entity linking with a novel text-to-text or"prompt"based method that uses generative language models such as T5 and GPT. We achieve encouraging results with this new approach.
Mode detection in vehicles using a sliding mode estimator

Abstract This paper explores the use of sliding mode observers to detect the onset of potentially dangerous vehicle modes such as oversteer, understeer or split-mu braking. Provided these modes can be detected quickly enough, existing stability controllers can be engaged to ensure safe performance of the vehicle. It is shown that the equivalent output error injection signals associated with the sliding mode observer have distinctive signatures depending on the particular mode which is encountered. Appropriate thresholds on these signals can be set so the scheme ignores variations which arise during the course of normal driving, but can detect and isolate different undesirable vehicle modes within 0.3 seconds of their onset.
"Interconnection between SDN Adhoc Controllers"

: Recent and famous method to computer the architecture of network monitoring and controlling the whole network providing several qualities is the object of Software Defined Networking (SDN) is programmatically and centrally. Nevertheless, deployment of SDN in broad scale networks of various operators and service offers is constrained because of lack of standardized connection between the controllers of SDN and use of routing algorithms of conventional networks. In addition, different networks that use adhoc technologies need special management in the combination between software Defined networks (SDN) and heterogeneous Networks. An examination of SDN basics, present resolution and ways to scale their performance for various networks are presented in this dissertation, and we shape problems of SDN field interconnection for eastwest communication with relying on the examination. We suggest, to find a solution for this problem, a new architecture for correlation of controllers in different fields of SDN for development of SDN to allow controllers of SDN and applications in multi-cluster or field networks to coordinate in a higher quality.
Team ITTC at BioCreative VII LitCovid Track 5: combining pre-trained and bag-of-words models

—In this report we present the results of our experiments for the BioCreative VII Track 5 challenge organized by the US NIH / National Library of Medicine. The task, based on a sample from a manually curated database of COVID-19 studies [3], is to automatically assign one or more out of 8 topic labels (such as Diagnosis , Treatment , Transmission ) based on an article’s abstract, title and metadata. We have evaluated several machine learning methods and the best result was obtained by combining a bag-of-words approach based on Support Vector Machines and a BERT based model pre-trained in the biomedical domain.
The Adoption and Utilization of Electronic Business in Response to the Global Economy During COVID-19

The COVID-19 epidemic has triggered unmatched impairment to businesses globally. There are unmeasurable financial influences in the short-term and long-term and have causes intangible destruction within businesses. This study investigates the adoption and utilization of e-business during COVID-19 by both organizations and the general populaces. The study used a questionnaire-based survey to collect data from top managers of business organizations and their clients. SPSS was used to analyze the adoption factors. The outcomes presented that embracing e-business can assist to reduce the spread of COVID-19 and can reduce the physical ways of doing business. The findings of this study will help strategy makers, companies, and officials in making better decisions on the implementation of e-business. This will reduce the rapid spread of community transmission since ordering goods and services can easily be done virtually without physical contact, which goes in line with the social distance policy and in return boost the country’s economy
The overview of the NLM-Chem BioCreative VII track Full-text Chemical Identification and Indexing in PubMed articles

— The BioCreative NLM-Chem track calls for a community effort to fine-tune automated recognition of chemical names in biomedical literature. Chemical names are one of the most searched biomedical entities in PubMed and – as highlighted during the COVID-19 pandemic – their identification may significantly advance research in multiple biomedical subfields. While previous community challenges focused on identifying chemical names mentioned in titles and abstracts, the full text contains valuable additional detail. We organized the BioCreative NLM-Chem track to call for a community effort to address automated chemical entity recognition in full-text articles. The track consisted of two tasks: 1) Chemical Identification task, and 2) Chemical Indexing prediction task. For the Chemical Identification task, participants were expected to predict with high accuracy all chemicals mentioned in recently published full-text articles, both span (i.e., named entity recognition) and normalization (i.e., entity linking) using MeSH. For the Chemical Indexing task, participants identified which chemicals should be indexed as topics for the article's topic terms in the NLM article and indexing, i.e., appear in the listing of MeSH terms for the document. This manuscript summarizes the BioCreative NLM-Chem track. We received a total of 88 submissions in total from 17 teams worldwide. The highest performance achieved for the Chemical Identification task was 0.8672 f-score (0.8759 precision, 0.8587 recall) for strict NER performance and 0.8136 f-score (0.8621 precision, 0.7702 recall) for strict normalization performance. The highest performance achieved for the Chemical Indexing task was 0.4825 f-score (0.4397 precision, 0.5344 recall). The NLM-Chem track dataset and other challenge materials
Prediction of COVID-19 severity in Madrid region with Model-leaf trees

— The global pandemic caused by the Coronavirus Disease 2019 has affected society at severe levels. Among the major problems brought by it, hospital overcrowding was one of the most dangerous. This paper presents Model-leaf trees, a new machine learning classification algorithm that combines decision trees with other classification models at its leaves to create an enhanced solution. The model will be tested using COVID-19 data from 1632 patients from hospitals belonging to Fundación Jiménez Díaz, and one of the generated models will be interpreted and presented as a viable solution that could be used in a real scenario.
Extending Adaptive Spacing Heuristics to MultiSkill Items

Adaptive spacing algorithms are powerful tools for helping learners manage their study time efficiently. By personalizing the temporal distribution of retrieval practice of a given piece of knowledge, they improve learners' long-term memory retention compared to fixed review schedules. However, such algorithms are generally designed for the pure memorization of single items, such as vocabulary words. Yet, the spacing effect has been shown to extend to more complex knowledge, such as the practice of mathematical skills. In this article, we extend three adaptive spacing heuristics from the literature for selecting the best skill to review at any timestamp given a student's past study history. In real-world educational settings, items generally involve multiple skills at the same time. Thus, we also propose a multi-skill version for two of these heuristics: instead of selecting one single skill, they select with a greedy procedure the most promising subset of skills to review. To compare these five heuristics, we develop a synthetic experimental framework that simulates student learning and forgetting trajectories with a student model. We run multiple synthetic experiments on large cohorts of 500 simulated students and publicly release the code for these experiments. Our results highlight the strengths and weaknesses of each heuristic in terms of performance, robustness, and complexity. Finally, we find evidence that selecting the best subset of skills yields better retention compared to selecting the single best skill to review.
Impact Analysis and Mitigation of Losing Time Synchronization at Micro-PMUs in Event Location Identification

Prior studies have shown that most phasor measurement units (PMUs) in practice suffer from some level of time synchronization loss at least once every day. We address this issue in the context of distribution-level PMUs, i.e., micro-PMUs, and with focus on the application of micro-PMUs in event location identification in situational awareness. We show that a state-of-the-art method that is highly successful in identifying the correct event location when the micro-PMUs are synchronized, fails when time synchronization is lost among the micro-PMUs. An alternative method is proposed to identify the location of events not only when the micro-PMUs are time synchronized but also when micro-PMUs lose time synchronization. The proposed model works for different scenarios for losing time synchronization among some or all micro-PMUs.
WOOF: user deﬁned news recommendation system

Project WOOF! incorporates a personalized news recommendation system and increases users satisfaction rate. The objective of the system is to independently crawl news web-sites, ﬁlter out redundant news, classify the news into system generated and user speciﬁc categories and provide the user with a short summary of the news. The system also would incorporate collaborative ﬁlter techniques that can be captured using implicit user actions on the website. By crawling the 8 major news websites from March 13th to May 9th, WOOF has collected more the 12,000 cutting edges news from all aspect of the world. Our works are based on these news.
* F 2 - Rules for Qualification of Developing and Managing Software Product Line

Software product line has emerged as a valuable concept of developing software based on reusable software assets. The concept aims on effective utilization of software assets, reduced time to delivery, improved quality and better benefits to cost ratio of products. In this paper we have defined certain rules for the qualification of developing and managing a software product line. An organization must follow these rules in order to establish and manage software product line effectively.
“Measure the Difference between Current and Expected Performance using Machine Learning (For Autonomous Driving Systems)”

: Measuring the performance of the program is one of the most important stages of testing, as we first test the program to detect problems that may occur to the user in the future, and these problems are the difference between the performance of the program and what is required of it, and we search for the difference and from it the program is evaluated and to measure this difference we need to measure Performance with measurable and comparable measures The problem with this is that we need to measure something qualitative, which is the quality of the program with a quantitative thing, which is its standards, and if the quality is calculated by reducing the difference between what is expected and what is in place, the problem arises in how to define what is expected, especially since the subject goes beyond defining what It is required because the program can have achieved all the points required of it in the current circumstances, but what happens if the circumstances and capabilities change in the future, of course, there are many methods and models that help in evaluating the program’s performance in the future through different tests, but it is not possible to measure all possibilities as well. Using the human element in conducting this research increases the time and cost, especially if it deals with a large number of possibilities and special circumstances
Promoting Malaysian Government Universities: PotentialStudents’ Views

This study sets out to determine the choice of Malaysian Universities by Form Five and Form Six students in Malaysia. Further to that, respondents were asked of their choice of branding statements and promotion tools for Universities. The top three Universities chosen as first choice were UM, USM and UKM. The top three branding statements overall were excellent teaching staff, excellent resource and safety. The top three promotion tools were teachers, newspapers and parents. Recommendations were made to Universities.
The Use of the Growth-Share Matrix in Strategic Planning

In the second of a series of three tutorials, the methodology and strategic implications of the portfolio business matrix are analyzed and illustrations given of the use of the growth-share matrix. Further refinements as well as warnings to prevent misleading conclusions are offered, and the new matrix advanced by BCG for the 1980's is outlined.
On the Integrability of Pfaffian Forms on ${\mathbb R}^{n}$

This paper details the lesser known conditions onRn for the integrability of pfaffian forms, or 1-forms. Emphasis is given to locality of these conditions, and proofs in some additional detail are provided for theorems due to Clairaut and Carathéodory. Considering the importance of the integrability of pfaffian forms, in particular in mathematicalphysics, this paper shows that: there is a hidden content in Carathéodory’s theorem in the direction of a global integrability.
Nonlinear static and dynamic analyses of reinforced concrete buildings - comparison of different modelling approaches

It generally accepted that most building structures shall exhibit a nonlinear response when subjected to medium-high intensity earthquakes. It is currently known, however, that this phenomenon is not properly modelled in the majority of cases, especially at the design stage, where only simple linear methods have effectively been used. Recently, as a result of the exponential progress of computational tools, nonlinear modelling and analysis have gradually been brought to a more promising level. A wide range of modelling alternatives developed over the years is hence at the designer`s disposal for the seismic design and assessment of engineering structures. The objective of the study presented herein is to test some of these models in an existing structure, and observe their performance in nonlinear static and dynamic analyses. This evaluation is done by the use of two of a known range of advanced computer programs: SAP2000 and SeismoStruct. The different models will focus on the element flexural mechanism with both lumped and distributed plasticity element models. In order to appraise the reliability and feasibility of each alternative, the programs capabilities and the amount of labour and time required for modelling and performing the analyses are also discussed. The results obtained show the difficulties that may be met, not only in performing nonlinear analyses, but also on their dependency on both the chosen nonlinear structural models and the adopted computer programs. It is then suggested that these procedures should only be used by experienced designers, provided that they are aware of these difficulties and with a critical stance towards the result of the analyses.
Broadband Continuous-Variable Entanglement Generation Using a Kerr-Free Josephson Metamaterial

Entangled microwave photons form a fundamental resource for quantum information processing and sensing with continuous variables. We use a low-loss Josephson metamaterial comprising superconducting, nonlinear, asymmetric inductive elements to generate frequency-entangled photons from vacuum ﬂuctuations at a rate of 2 giga entangled bits per second spanning over the 4-GHz bandwidth. The device is operated as a traveling-wave parametric ampliﬁer under Kerr-relieving biasing conditions. Furthermore, we demonstrate single-mode squeezing in such devices—3.1 ± 0.7 dB below the zero-point level at half of modulation frequency.
Enhanced Formulation for Guillotine 2D Cutting Problems

We advance the state of the art in Mixed-Integer Linear Programming (MILP) formulations for Guillotine 2D Cutting Problems by (i) adapting a previously known reduction to our preprocessing phase and by (ii) enhancing a previous formulation by cutting down its size and symmetries. Our focus is the Guillotine 2D Knapsack Problem with orthogonal and unrestricted cuts, constrained demand, unlimited stages, and no rotation -- however, the formulation may be adapted to many related problems. The code is available. Concerning the set of 59 instances used to benchmark the original formulation, and summing the statistics for all models generated, the enhanced formulation has only a small fraction of the variables and constraints of the original model (respectively, 3.07% and 8.35%). The enhanced formulation also takes about 4 hours to solve all instances while the original formulation takes 12 hours to solve 53 of them (the other six runs hit a three-hour time limit each). We integrate, to both formulations, a pricing framework proposed for the original formulation; the enhanced formulation keeps a significant advantage in this situation. Finally, in a recently proposed set of 80 harder instances, the enhanced formulation (with and without the pricing framework) found: 22 optimal solutions for the unrestricted problem (5 already known, 17 new); 22 optimal solutions for the restricted problem (all are new and they are not the same 22 of the optimal unrestricted solutions); better lower bounds for 25 instances; better upper bounds for 58 instances.
Generalizable Cross-Graph Embedding for GNN-based Congestion Prediction

Presently with technology node scaling, an accurate prediction model at early design stages can significantly reduce the design cycle. Especially during logic synthesis, predicting cell congestion due to improper logic combination can reduce the burden of subsequent physical implementations. There have been attempts using Graph Neural Network (GNN) techniques to tackle congestion prediction during the logic synthesis stage. However, they require informative cell features to achieve reasonable performance since the core idea of GNNs is built on the message passing framework, which would be impractical at the early logic synthesis stage. To address this limitation, we propose a framework that can directly learn embeddings for the given netlist to enhance the quality of our node features. Popular random-walk based embedding methods such as Node2vec, LINE, and DeepWalk suffer from the issue of cross-graph alignment and poor generalization to unseen netlist graphs, yielding inferior performance and costing significant runtime. In our framework, we introduce a superior alternative to obtain node embeddings that can generalize across netlist graphs using matrix factorization methods. We propose an efficient mini-batch training method at the sub-graph level that can guarantee parallel training and satisfy the memory restriction for large-scale netlists. We present results utilizing open-source EDA tools such as DREAMPLACE and OPENROAD frameworks on a variety of openly available circuits. By combining the learned embedding on top of the netlist with the GNNs, our method improves prediction performance, generalizes to new circuit lines, and is efficient in training, potentially saving over 90% of runtime.
Hybrid Saturation Restoration for LDR Images of HDR Scenes

There are shadow and highlight regions in a low dynamic range (LDR) image which is captured from a high dynamic range (HDR) scene. It is an ill-posed problem to restore the saturated regions of the LDR image. In this paper, the saturated regions of the LDR image are restored by fusing model-based and data-driven approaches. With such a neural augmentation, two synthetic LDR images are first generated from the underlying LDR image via the model-based approach. One is brighter than the input image to restore the shadow regions and the other is darker than the input image to restore the high-light regions. Both synthetic images are then refined via a novel exposedness aware saturation restoration network (EASRN). Finally, the two synthetic images and the input image are combined together via an HDR synthesis algorithm or a multi-scale exposure fusion algorithm. The proposed algorithm can be embedded in any smart phones or digital cameras to produce an information-enriched LDR image.