Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum: A New Kind of Knowledge Discovery

While the first solid-state device (known as transistor) was being developed at Bell lab in the midtwentieth to replace vacuum-tubes [30], artificial intelligence (AI) was being conceptualized by a generation of scientists, mathematicians, and philosophers. In 1950, Alan Turing suggested two criteria for machine intelligence: memory for enabling machines to store and retrieve data, and reasoning (i.e., having the capacity to process data) [31]. Since then, trends in doubling the transistor count, characterized by Moore’s law, have catalyzed AI advancements. Nowadays, AI applications have access to not only large-scale memories but also high-performance computing (HPC) resources. After decades of predomination, the era of Moore’s law is drawing to a close. Are we prepared for the end of Moore’s law? Can digital systems keep pace with ever-increasing demand for data storage and information processing capacity? The microelectronics industry (will be known as the nanoelectronics industry in the near-term) trying to identify new materials and devices to replace the 50 years old transistor technology—including, but not limited to, non-classical CMOS (such as new channel materials), and alternatives to CMOS (e.g., spintronics, single-electron devices, and molecular computing) [30]. Although the microelectronics industry will continue to reduce electronic devices’ costs, there are theoretical boundaries that can limit future micro/nano-electronic processing devices’ computing power. However, it is worth noting that AI applications can still benefit from the dramatic increase in memory systems’ speed and storage capacity. Transitioning from vector-based computation, in central processing units (CPUs), to matrix-based computation resulted in the emergence of graphical processing units (GPUs) that have reshaped the landscape for accelerated computing, namely in realms of scientific computing, high-performance computing, machine learning, and big data analytics. In the same manner, custom design applicationspecific integrated circuits (ASICs) for extending computations (from vectors and matrices) to tensors (i.e., complex and higher-order objects), can repeat providing a disruptive capability. As an example, the tensor processing unit (TPU), by Google, is an accelerator for near-real-time deep learning applications with low latency that has demonstrated throughput improvements of over 15–30 and power efficiency improvement of 30–70 over current CPUs and Kepler generation of GPUs, albeit lower precision computations [17]. The supercomputing community tries to address the limits in shrinking transistors’ size through parallelism (i.e., distributing data and processes), which has emerged a new kind of race (or war) between the United States, China, and perhaps other players like Russia. Besides national security and economic concerns in developing the next generation of supercomputers, the near-term future of AI can mainly depend on the result of the supercomputing race. Hence, we can expect that the winner of the supercomputing game will achieve AI supremacy.
Measuring Sustainability Consciousness in Italy

The most common notion sees sustainability as an ideal and necessary state in which society, the environment and the economy can thrive without harming each other in the present, the future and the global space. In this paper, using data from a sample of respondents, we validate the Italian version of the Sustainability Consciousness Questionnaire developed by a Swedish research group (Gericke et al., 2019), which aims to measure sustainability consciousness as an individual experience and awareness of sustainability. The validation process relies on the Hierarchical Confirmatory Factor Analysis model of estimation and includes both the long and short forms of the questionnaire proposed in the original study. Specifically, we propose two alternative approaches to validating the short form, one based on the same subset of items as the original proposal, and another based on a data-driven strategy, resulting in a different subset of items. After validation at the national level, the Sustainability Consciousness Questionnaire represents a useful tool for researchers and policymakers to assess how aware and responsible a national or local population is regarding such an important and urgent global goal.
Multitasking with BERT

Models take a lot of resources to train. Thus it is often desirable to train one single model that can perform a variety of tasks, which is what we are trying to achieve on this default final project. Our goal is to implement a BERT model that is able to multitask: after training, this one single model would be able to perform sentiment analysis, paraphrase detection, and semantic textual similarity. After finishing implementing a minBERT model and the Adam optimizer, we achieve this multi-task goal by implementing a multi-task finetuning framework, which optimizes the model over all three different tasks at the same time instead of one after another. To avoid clashing between objectives, we implemented gradient surgery on our Adam optimizer and explored whether assigning different weights to objectives could lead to a potential increase in model performance. The answer we get from this particular model is no. We take full advantage of the Sentence-BERT framework for paraphrase detection and semantic textual similarity tasks, which captures deeper information and yields a much higher model performance than the conventional concatenation approach. We also propose using a uniform loss function across multi-task learning, and we discovered that this provides uniformity to the losses of distinct objectives and empirically yields better performance on the multitask-fine tuned model.
RockStartIT: Authentic and Inclusive Interdisciplinary Software Engineering Courses

The RockStartIT initiative was explicitly designed to increase the representation of girls in software engineering (SE) by providing interdisciplinary courses that connect the subject matter to their interests and passions. The program has been successful in engaging a diverse group of high school students, encouraging them to explore computer science (CS) through problem-solving in the areas of their choice. The program’s structure and approach have been instrumental in promoting interest in CS and increasing the understanding of the importance of using CS to solve real-world, interdisciplinary problems. The results of surveys conducted with program participants have shown that the program has been successful in providing girls with an alternative pathway into the field of computer science by building on their individual interests, which would not have been possible without the interdisciplinary approach.
Digests, snapshots, events, or cumulative gaze - what is most informative of user success and failure? A study of the foretelling signs of user performance during interaction with visualizations of ontology class

The current research landscape in ontology visualization has largely focused on tool development yielding an extensive array of visualization tools. Although many existing solutions provide multiple ontology visualization layouts, there is limited research in adapting to an individual user’s performance, despite successful applications of adaptive technologies in related fields including information visualization. In an effort to innovate beyond traditional one-size-fits-all ontology visualizations, this paper contributes one step towards realizing user adaptive ontology visualization in the future by recognizing timely moments where users may potentially need intervention, as real-time adaptation can only occur if it is possible to correctly predict user success and failure during an interaction in the first place. Building on a wealth of research in eye tracking, this paper compares four approaches to predictive gaze analytics through a series of experiments that utilizes scheduled gaze digests, irregular gaze events, last known gaze status, as well as all gaze captured for a user at a given moment in time. Experimental results suggest that irregular gaze events are most informative of early predictions, while increased gaze is most often associated with peak accuracies. Furthermore, cognitive workload appears to be most indicative of overall user performance, while task type may impact predictive outcome irrespective of the gaze analysis approach in use.
BERT-based Multi-task Learning

While large language models like BERT[1] have greatly advanced language modeling, effective multi-task learning with generalizable sentence embeddings continues to be an important area of research. We implemented a BERT Transformers [2] model that predicts sentiment, detects paraphrases and predicts semantic textual similarity (STS). On the test sets our extended-BERT model achieves 86.5% accuracy on paraphrase detection, 50% accuracy on sentiment analysis, and an STS correlation coefficient of 76.1%, for an average metric of 70.9%. We found that for the tasks with small datasets, heavy regularization via SMART [3] and leveraging additional in-domain datasets via SimCSE contrastive loss [4] were paramount to improving perfomance. We further found that head architecture is very important, and that by prompting the model we were able to expedite learning. Our implementations of our extensions resulted in an approximately 12% improvement over our base-BERT model.
Simultaneous Estimation of Nebivolol and Hydrochlorothiazide in Combined Tablet Dosage Form by Multicomponent Mode of Analysis

A simple, accurate, precise and economical procedure for simultaneous estimation of Nebivolol hydrochloride and Hydrochlorothiazide in two component tablet dosage form has been developed utilizing concept of standard addition. The method is based upon determination of Nebivolol at 281 nm and Hydrochlorothiazide at 272 nm, in aqueous methanol (20% v/v). Nebivolol hydrochloride and Hydrochlorothiazide at their respective λmax 281.0 nm and 272.0 nm shows linearity in the concentration range of 5–35 μg/ml and 10–70 μg/ml respectively. The method was validated statistically. Recovery study was performed to confirm the accuracy of the method.
Explorative Study on Semantically Resonant Colors for Combinations of Categories with Application to Meteorological Data

We present an exploratory study of semantically resonant colors for combinations of categories. The goal is to support color selection of multi-labeled classes of classified data. We asked participants to assign colors to different categories in the meteorological domain and then to their combinations. Our results show that the colors chosen for the combinations are related to the colors for the individual categories. We also found indications that people tend to prefer darker color values for combinations of categories. Our results can be used to color code meteorological data.
IAKG: Importance-augmented Knowledge Graphs

In recent years, Pre-trained Language Models (PLMs) have seen a meteoric rise, largely because through their training process, PLMs form a rich, implicit representation of domain knowledge. Yet its implicit nature makes knowledge in-scrutable, which calls the need for explicit representations like Knowledge Graphs (KGs). KGs, while powerful in representing complex relationships between entities, do not capture priority between triples. In this paper, we propose a method for augmenting KGs with importance weights using Wikipedia as proxy for importance. As Wikipedia is limited as a dataset, we use PLMs to generalize the notion of importance from the Wikipedia dataset. We see importance-augmented knowledge graph as an important step in constructing richer explicit representations of knowledge.
Neural Locality Sensitive Hashing for Blocking in Entity Blocking

Locality-sensitive hashing (LSH) is an algorith-001 mic technique that hashes similar input items 002 into the same “buckets” with high probabil-003 ity. It is a basic primitive in several large-scale 004 data processing applications, including nearest-005 neighbor search, entity resolution, clustering, 006 etc. In this work, we focus on the blocking 007 phase in the entity resolution task. The goal of 008 blocking is to avoid comparing all entity pairs 009 by filtering out unmatched pairs. For this pur-010 pose, existing LSH functions that are based on 011 generic similarity metric like Jaccard similar-012 ity, can only capture the occurrence of words 013 while the semantics of the texts are ignored. 014 On the other hand, several work have proposed 015 to use language models to vectorize the data 016 items and use the similarity of embeddings to 017 find candidate pairs. However, it is still a chal-018 lenge to fine-tune the language models so that 019 the obtained embeddings can precisely capture 020 the similarity of item pairs for ranking purpose. 021 To this end, we propose NLSHBlock (Neural-022 LSH Block), a blocking approach that is based 023 on pre-trained language models and fine-tuned 024 with a novel LSH-inspired loss function. We 025 evaluate the performance of Neural-LSH on the 026 blocking stage of entity resolution, and show 027 that it out-performs existing methods by a large 028 margin on a wide range of datasets. 029
GPThreats-3: Is Automatic Malware Generation a Threat?

Recent research advances introduced large textual models, of which GPT-3 is state-of-the-art. They enable many applications, such as generating text and code. Whereas the model's capabilities might be explored for good, they might also cause some negative impact: The model's code generation capabilities might be used by attackers to assist in malware creation, a phenomenon that must be understood. In this work, our goal is to answer the question: Can current large textual models (represented by GPT-3) already be used by attackers to generate malware? If so: How can attackers use these models? We explore multiple coding strategies, ranging from the entire mal ware description to separate descriptions of mal ware functions that can be used as building blocks. We also test the model's ability to rewrite malware code in multiple manners. Our experiments show that GPT-3 still has trouble generating entire malware samples from complete descriptions but that it can easily construct malware via building block descriptions. It also still has limitations to understand the described contexts, but once it is done it generates multiple versions of the same semantic (malware variants), whose detection rate significantly varies (from 4 to 55 Virustotal AV s).
MPT: Multimodal Prompt Tuning for Event Detection

Event Detection is a key and challenging sub-001 task of event extraction, which has serious trig-002 ger word ambiguity. Existing studies mainly 003 focus on contextual information in text, while 004 there are naturally many images in news ar-005 ticles that need to be explored. We believe 006 that images not only reflect the core events of 007 the text but also help to trigger word disam-008 biguation. In this paper, we propose a new 009 bi-recursive multimodal Prompt Tuning (MPT) 010 model for deep interaction between images and 011 sentences to achieve aggregation of modal fea-012 tures. MPT uses pre-trained CLIP to encode 013 and map sentences and images into the same 014 multimodal semantic space and uses alternat-015 ing dual attention to select information features 016 for mutual enhancement. Then, a soft prompt 017 method of multimodal guidance is proposed, 018 and the multimodal information obtained by 019 fusion is used to guide the downstream event 020 detection task. Our superior performance com-021 pared to six state-of-the-art baselines and fur-022 ther ablation studies, demonstrate the impor-023 tance of image modality and the effectiveness 024 of the proposed architecture. 025
Implementing Projected Attention Layers (PALs) for Joint Multi-Task Learning

Joint multi-task learning shares operations and parameters across tasks, potentially saving computing time, energy, and storage compared to separately fine-tuning one model for each task. However, this can come at the cost of poorer task-specific performance. We explore ways of overcoming this challenge by adapting a base BERT model for joint learning of sentiment classification, paraphrase detection, and evaluation of semantic similarity. We do this by (i) implementing Projected Attention Layers (PALs), which are small, task-specific, multi-head attention layers that work alongside shared BERT layers, and (ii) trying different rates of sampling across tasks during training. We find that average performance across tasks improves when using PALs along with annealed sampling, a method that initially samples more from tasks with large datasets but samples more equally across tasks as training progresses. We also find that when the tasks we jointly train on are similar, annealed sampling seems to contribute more to performance improvements than PALs. However, if a task is dissimilar to the others and has a relatively small dataset, using either PALs or annealed sampling on their own can worsen performance, and it is important to use both together to dampen this effect. Task-specific performance can also be improved by task-specific pre-training after, but not before, joint multi-task fine-tuning.
Isolated and Exhausted: Attacking Operating Systems via Site Isolation in the Browser

Site Isolation [12,40] is a security architecture for browsers to protect against side-channel and renderer exploits by sep-arating content from different sites at the operating system (OS) process level. By aligning web and OS security boundaries, Site Isolation promises to defend against these attack classes systematically in a streamlined architecture. However, Site Isolation is a large-scale architectural change that also makes OS resources more accessible to web attackers, and thus exposes web users to new risks at the OS level . In this paper, we present the ﬁrst systematic study of OS resource exhaustion attacks based on Site Isolation, in the web attacker model , in three steps: (1) ﬁrst-level resources directly accessible with Site Isolation; (2) second-level resources whose direct use is protected by the browser sandbox; (3) an advanced, real-world attack. For (1) we show how to create a fork bomb , highlighting conceptual gaps in the Site Isolation architecture. For (2) we show how to block all UDP sockets in an OS, using a variety of advanced browser features. For (3), we implement a fully working DNS Cache Poisoning attack based on Site Isolation, building on (2) and bypassing a major security feature of DNS. Our results show that the interplay between modern browser features and older OS features is increasingly problematic and needs further research.
Embedding Freedom? An NLP Approach to Uncovering Pre- and Post-Abolition Racial Bias in Brazilian Literature

We use word embeddings trained with word2vec on 81 Brazilian literary works published in the periods before and after 1888, when slavery was abolished, to uncover the racial biases present at the time and how they evolved. We adapt the metric of bias that uses vector geometry proposed by Garg et al. (2018). First, we derive the bias for a set of concepts of towards Blacks or whites, finding that concepts such as ugly, submissive, and dirty are biased towards Blacks, with greater bias magnitude before abolition. Second, we analyze pairs of words, such as ugly and pretty, submissive and freed, and dirty and clean. The word vectors for Blacks are more biased towards the negative words in most pairs, with some biases accentuated post-abolition. Finally, we perform a leave-out analysis in which we rebuild the word2vec embeddings without the works of prominent authors from different literary movements, one author at a time. There is heterogeneity on the type and magnitude of bias that authors bring to the word embeddings, and the biases are not always a reflection of the author’s values.
The Syntax and Semantics of the Quotative Complement in Japanese

I will consider the quotative complement (QC) in Japanese, which is prima facie introduced by -to, a reporting suffix. In the literature, it has been widely assumed that -to is a (reporting) complementizer and that it introduces clausal complements (cf. Saito 2010). However, I will propose that there is a hidden structure in the QC in Japanese, arguing that it is an invisible verbal phrase headed by a phonologically null verb, SAY. This verb is presumably derived/grammaticalized from a lexical verb, iw‘say’, and, there are versions of Japanese where it can be overt. As we will see, the analysis to be proposed captures two important facts regarding the QC: (i) why -to can embed various linguistic expressions via indirect quotation, and (ii) why -to is used in both direct and indirect quotations. This working paper is available in University of Pennsylvania Working Papers in Linguistics: https://repository.upenn. edu/pwpl/vol29/iss1/21 The Syntax and Semantics of the Quotative Complement in Japanese
KDAlloc : The KLEE Deterministic Allocator

The memory allocator can have an important impact in symbolic execution. Taking a user-centric view, this tool demonstration paper discusses some of the main bene ts provided by KLEE’s new allocator KDAlloc in terms of improved deterministic execution and bugnding capabilities. We then introduce a new replay tool for KLEE which enables the native execution to integrate KDAlloc and receive the same heap addresses as during symbolic execution.
Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Approach

Machine learning problems with multiple objectives appear either i) in learning with multiple criteria where learning has to make a trade-off between multiple performance metrics such as fairness, safety and accuracy; or, ii) in multi-task learning where multiple tasks are optimized jointly, sharing inductive bias among them. These multiple-objective learning problems are often tackled by the multi-objective optimization framework. However, existing stochastic multi-objective gradient methods and their recent variants (e.g., MGDA, PCGrad, CAGrad, etc.) all adopt a biased gradient direction, which leads to degraded empirical performance. To this end, we develop a stochastic multi-objective gradient correction (MoCo) method for multi-objective optimization. The unique feature of our method is that it can guarantee convergence without increasing the batch size even in the nonconvex setting. Simulations on supervised and reinforcement learning demonstrate the effectiveness of our method relative to state-of-the-art methods.
Deep Learning Risk Prediction of Bloodstream Infection in the Intensive Care Unit

Bloodstream infection is a leading cause of mortality in patients in the intensive care unit (ICU). Early detection and treatment of bloodstream infection is associated with significantly better clinical outcomes. However, the detection of bloodstream infection requires a time-consuming blood culture. Far more blood cultures are ordered than return positive; this excess of blood cultures can cause delays in result and therefore treatment. In this study, we aim to move towards a continuous monitoring tool to help doctors and nurses in the ICU identify which patients require a blood culture, with the goal of supporting earlier detection and treatment of bloodstream infection. We formulated this goal as a multivariate time-series classification problem and applied powerful predictive deep learning approaches to model multivariate time-series data. We used a variety of model validation and explainability techniques to help understand the decisions of these deep learning models and promote trust in their predictions.