Reviewerly/exHarmony huggingface dataset:
Efficient carry select 16-bit square root adder with complementary metal-oxide semiconductor implementation

The adder is the maximum usually used mathematics block in programs inclusive of &lt;span lang="EN-US"&gt;central processing unit (CPU) and virtual sign processing. As a result, it is important to expand a space-saving, low-strength, high-overall performance adder circuit. The hassle is diagnosed to layout mathematics sub structures with minimized strength dissipation, low area, and minimal time postpone of common-sense circuits. In conventional &lt;a name="_Hlk95894088"&gt;&lt;/a&gt;carry select adder (CSA), the time required to generate the sum output is less than other basic adder circuits but the principal difficulty is the location because the variety of transistors used to put in force the CSA circuit is fairly more. So, the area increases because of which the overall power consumption of the circuit will be more. If it's far viable to lessen the variety of transistors used withinside the structure of CSA adder, then, the strength intake of the circuit may be decreased or even the reaction time will improve. By lowering the area of the adder circuit, the suggested solution intends to reduce power consumption and latency.&lt;/span&gt;
Attention-Based Recommendation On Graphs

Graph Neural Networks (GNN) have shown remarkable performance in different tasks. However, there are a few studies about GNN on recommender systems. GCN as a type of GNNs can extract high-quality embeddings for different entities in a graph. In a collaborative filtering task, the core problem is to find out how informative an entity would be for predicting the future behavior of a target user. Using an attention mechanism, we can enable GCNs to do such an analysis when the underlying data is modeled as a graph. In this study, we proposed GARec as a model-based recommender system that applies an attention mechanism along with a spatial GCN on a recommender graph to extract embeddings for users and items. The attention mechanism tells GCN how much a related user or item should affect the final representation of the target entity. We compared the performance of GARec against some baseline algorithms in terms of RMSE. The presented method outperforms existing model-based, non-graph neural networks and graph neural networks in different MovieLens datasets.
Tripartite State Characterization via Activated Bipartite Entanglement

We propose a procedure to identify and classify genuine tripartite entanglement in pure 3-qubit states via the Activated Bipartite Entanglement (ABE), which is defined here as the difference between the Entanglement of Assistance and the Entanglement of Formation. We show that for pure states belonging to one of the two inequivalent classes of genuine tripartite entanglement, i.e., GHZ or W states, the ABE is always greater than zero. For separable and biseparable states it is always null. In addition, our approach is capable to distinguish between genuine tripartite entangled states, those belonging to the GHZ class from those belonging to the W class. We also present an experimental proposal, by using linear optical circuits and internal degrees of freedom of a single photon, to measure the ABE and to verify the characterization via activated entanglement. The circuit simulation shows an excellent agreement with theoretical prediction for a wide class of GHZ and W states.
Interpreting Lambda Calculus in Domain-Valued Random Variables

We develop Boolean-valued domain theory and show how the lambda-calculus can be interpreted in using domain-valued random variables. We focus on the reflexive domain construction rather than the language and its semantics. The notion of equality has to be interpreted in the Boolean algebra and when we say that an equation is valid in the model we mean that its interpretation is the top element of the Boolean algebra.
A comparative study of several ADPCM schemes with linear and nonlinear prediction

In this paper we compare several ADPCM schemes with nonlinear prediction based on neural nets with the classical ADPCM schemes based on several linear prediction schemes. Main studied variations of the ADPCM scheme with adaptive quantization (2 to 5 bits) are: -forward vs backward -sample adaptive vs block adaptive
Extending the Vocabulary of Fictional Languages using Neural Networks

Fictional languages have become increasingly popular over the recent years appearing in novels, movies, TV shows, comics, and video games. While some of these fictional languages have a complete vocabulary, most do not. We propose a deep learning solution to the problem. Using style transfer and machine translation tools, we generate new words for a given target fictional language, while maintaining the style of its creator, hence extending this language vocabulary.
Active Weighted Aging Ensemble for Drifted Data Stream Classification

One of the significant problems of streaming data classification is the occurrence of concept drift, consisting of the change of probabilistic characteristics of the classification task. This phenomenon destabilizes the performance of the classification model and seriously degrades its quality. An appropriate strategy counteracting this phenomenon is required to adapt the classifier to the changing probabilistic characteristics. One of the significant problems in implementing such a solution is the access to data labels. It is usually costly, so to minimize the expenses related to this process, learning strategies based on semi-supervised learning are proposed, e.g., employing active learning methods indicating which of the incoming objects are valuable to be labeled for improving the classifier's performance. This paper proposes a novel chunk-based method for non-stationary data streams based on classifier ensemble learning and an active learning strategy considering a limited budget that can be successfully applied to any data stream classification algorithm. The proposed method has been evaluated through computer experiments using both real and generated data streams. The results confirm the high quality of the proposed algorithm over state-of-the-art methods.
Evaluating categorical encoding methods on a real credit card fraud detection database

Correctly dealing with categorical data in a supervised learning context is still a major issue. Furthermore, though some machine learning methods embody builtin methods to deal with categorical features, it is unclear whether they bring some improvements and how do they compare with usual categorical encoding methods. In this paper, we describe several well-known categorical encoding methods that are based on target statistics and weight of evidence. We apply them on a large and real credit card fraud detection database. Then, we train the encoded databases using state-of-the-art gradient boosting methods and evaluate their performances. We show that categorical encoding methods generally bring substantial improvements with respect to the absence of encoding. The contribution of this work is twofold: (1) we compare many state-of-the-art "lite" categorical encoding methods on a large scale database and (2) we use a real credit card fraud detection database.
Continual Test-Time Domain Adaptation

Test-time domain adaptation aims to adapt a source pre-trained model to a target domain without using any source data. Existing works mainly consider the case where the target domain is static. However, real-world machine perception systems are running in non-stationary and continually changing environments where the target domain distribution can change over time. Existing methods, which are mostly based on self-training and entropy regularization, can suffer from these non-stationary environments. Due to the distribution shift over time in the target domain, pseudo-labels become unreliable. The noisy pseudo-labels can further lead to error accumulation and catastrophic forgetting. To tackle these issues, we propose a continual test-time adaptation approach~(CoTTA) which comprises two parts. Firstly, we propose to reduce the error accumulation by using weight-averaged and augmentation-averaged predictions which are often more accurate. On the other hand, to avoid catastrophic forgetting, we propose to stochastically restore a small part of the neurons to the source pre-trained weights during each iteration to help preserve source knowledge in the long-term. The proposed method enables the long-term adaptation for all parameters in the network. CoTTA is easy to implement and can be readily incorporated in off-the-shelf pre-trained models. We demonstrate the effectiveness of our approach on four classification tasks and a segmentation task for continual test-time adaptation, on which we outperform existing methods. Our code is available at \url{https://qin.ee/cotta}.
Consistency and Coherence from Points of Contextual Similarity

Factual consistency is one of important summary evaluation dimensions, especially as summary generation becomes more fluent and coherent. The ESTIME measure, recently proposed specifically for factual consistency, achieves high correlations with human expert scores both for consistency and fluency, while in principle being restricted to evaluating such text-summary pairs that have high dictionary overlap. This is not a problem for current styles of summarization, but it may become an obstacle for future summarization systems, or for evaluating arbitrary claims against the text. In this work we generalize the method, and make a variant of the measure applicable to any text-summary pairs. As ESTIME uses points of contextual similarity, it provides insights into usefulness of information taken from different BERT layers. We observe that useful information exists in almost all of the layers except the several lowest ones. For consistency and fluency - qualities focused on local text details - the most useful layers are close to the top (but not at the top); for coherence and relevance we found a more complicated and interesting picture.
Redy: Remote Dynamic Memory Cache

Redy is a cloud service that provides high performance caches using RDMA-accessible remote memory. An application can customize the performance of each cache with a service level objective (SLO) for latency and throughput. By using remote memory, it can leverage stranded memory and spot VM instances to reduce the cost of its caches and improve data center resource utilization. Redy automatically customizes the resource configuration for the given SLO, handles the dynamics of remote memory regions, and recovers from failures. The experimental evaluation shows that Redy can deliver its promised performance and robustness under remote memory dynamics in the cloud. We augment a production key-value store, FASTER, with a Redy cache. When the working set exceeds local memory, using Redy is significantly faster than spilling to SSDs.
Faces: AI Blitz XIII Solutions

AI Blitz XIII Faces challenge hosted on www.aicrowd.com platform consisted of five problems: Sentiment Classification, Age Prediction, Mask Prediction, Face Recognition, and Face De-Blurring. Our team GLaDOS took second place. Here we present our solutions and results. Code implementation: https://github.com/ndrwmlnk/ai-blitz-xiii
Is Projection Mapping Natural? Towards Physical World Augmentation  Consistent with Light Field Context

Projection mapping seamlessly merges real and virtual worlds. Although much effort was made to improve its image qualities so far, projection mapping is still unnatural. We introduce the first steps towards natural projection mapping by making the projection results consistent with the light field context of our daily scene.
CryptoMiniSat Switches-Optimization for Solving Cryptographic Instances

Performing hundreds of test runs and a source-code analysis, we empirically identified improved parameter configurations for the CryptoMiniSat (CMS) 5 for solving crypto- graphic CNF instances originating from algebraic known-plaintext attacks on 3 rounds encryption of the Small AES-64 model cipher SR(3, 4, 4, 4). We finally became able to reconstruct 64-bit long keys in under an hour real time which, to our knowledge, has never been achieved so far. Especially, not without any assumptions or previous knowledge of key-bits (for instance in the form of side-channels, as in [11]). A statistical analysis of the non-deterministic solver runtimes was carried out and command line parameter combinations were defined to yield best runtimes which ranged from under an hour to a few hours in median at the beginning. We proceeded using an Automatic Algorithm Configuration (AAC) tool to systematically extend the search for even better solver configurations with success to deliver even shorter solving times. In this work we elaborate on the systematics we followed to reach our results in a traceable and reproducible way. The ultimate focus of our investigations is to find out if CMS, when appropriately tuned, is indeed capable to attack even bigger and harder problems than the here solved ones. For the domain of cryptographic research, the duration of the solving time plays an inferior role as compared to the practical feasibility of finding a solution to the problem. The perspective scalability of the here presented results is the object of further investigations.
Incremental Few-Shot Learning via Implanting and Compressing

This work focuses on tackling the challenging but realistic visual task of Incremental Few-Shot Learning (IFSL), which requires a model to continually learn novel classes from only a few examples while not forgetting the base classes on which it was pre-trained. Our study reveals that the challenges of IFSL lie in both inter-class separation and novel-class representation. Dur to intra-class variation, a novel class may implicitly leverage the knowledge from multiple base classes to construct its feature representation. Hence, simply reusing the pre-trained embedding space could lead to a scattered feature distribution and result in category confusion. To address such issues, we propose a two-step learning strategy referred to as \textbf{Im}planting and \textbf{Co}mpressing (\textbf{IMCO}), which optimizes both feature space partition and novel class reconstruction in a systematic manner. Specifically, in the \textbf{Implanting} step, we propose to mimic the data distribution of novel classes with the assistance of data-abundant base set, so that a model could learn semantically-rich features that are beneficial for discriminating between the base and other unseen classes. In the \textbf{Compressing} step, we adapt the feature extractor to precisely represent each novel class for enhancing intra-class compactness, together with a regularized parameter updating rule for preventing aggressive model updating. Finally, we demonstrate that IMCO outperforms competing baselines with a significant margin, both in image classification task and more challenging object detection task.
The Relation of Indonesian Language Learning to the Environment

In the current millennial generation, very many students often feel bored when participating in the learning process, because they are influenced by the world of sophisticated technology such as the use of gadgets. So pay attention to students who used to be eager to take part in learning, but now it is very difficult to increase their enthusiasm for learning, one of which is in the Indonesian language learning process. In the process of learning Indonesian, the environment greatly affects the quality of student learning. Learning Indonesian based on this environment is known to reduce the boredom experienced by students when participating in the process of teaching and learning activities. When students experience this, educators or teachers can invite students to take advantage of the surrounding environment as a fun learning medium. This environment-based learning can also increase students’ social correlation which includes values and procedures. This helps students redefine human relationships using the environment and be aware of and consider problems in the surrounding environment.
Extended Reality for Knowledge Work in Everyday Environments

Virtual and Augmented Reality have the potential to change information work. The ability to modify the workers senses can transform everyday environments into a productive office, using portable head-mounted displays combined with conventional interaction devices, such as keyboards and tablets. While a stream of better, cheaper and lighter HMDs have been introduced for consumers in recent years, there are still many challenges to be addressed to allow this vision to become reality. This chapter summarizes the state of the art in the field of extended reality for knowledge work in everyday environments and proposes steps to address the open challenges.
Insights Into Incitement: A Computational Perspective on Dangerous Speech on Twitter in India

Dangerous speech on social media platforms can be framed as blatantly inflammatory, or be couched in innuendo. It is also centrally tied to who engages it - it can be driven by openly sectarian social media accounts, or through subtle nudges by influential accounts, allowing for complex means of reinforcing vilification of marginalized groups, an increasingly significant problem in the media environment in the Global South. We identify dangerous speech by influential accounts on Twitter in India around three key events, examining both the language and networks of messaging that condones or actively promotes violence against vulnerable groups. We characterize dangerous speech users by assigning Danger Amplification Belief scores and show that dangerous users are more active on Twitter as compared to other users as well as most influential in the network, in terms of a larger following as well as volume of verified accounts. We find that dangerous users have a more polarized viewership, suggesting that their audience is more susceptible to incitement. Using a mix of network centrality measures and qualitative analysis, we find that most dangerous accounts tend to either be in mass media related occupations or allied with low-ranking, right-leaning politicians, and act as "broadcasters" in the network, where they are best positioned to spearhead the rapid dissemination of dangerous speech across the platform.
Connecting New Trends and New Skills for Academic Librarians

Guiding documents of professional associations are revealing the main future trends around the globe in information science. This study seeks to systematize and identify the skills expressed in them and aimed at information professionals. Thus, the content of the guiding documents is analyzed to find the trends for the area, systematizing them in areas of interest. The skills needed by the professionals that emanate from it are identified. In addition, a survey of future professionals (graduate students) is carried out to try to understand what skills are valued in the future performance of their profession. It is noted that it is important to be aware of current trends and recommendations for the sector. Therefore, the preparation of students in information science for the prospects for the future will be more appropriate and meaningful, bringing benefits to themselves and to the academic communities in which they operate, if future librarians in higher education become aware of a changing world in which they will have to act.
Computational complexity of one-variable fragments of products with T

We show that products of propositional modal logics containing the logic of reflexive frames T as a factor are embeddable into their single-variable fragments. The proof is a simplified version of the proof, to appear, of a similar result for products and expanding relativized products containing as a factor the logic KTB of reflexive and symmetric Kripke frames.
Quantum Architecture Search via Continual Reinforcement Learning

Quantum computing has promised significant improvement in solving difficult computational tasks over classical computers. Designing quantum circuits for practical use, however, is not a trivial objective and requires expert-level knowledge. To aid this endeavor, this paper proposes a machine learning-based method to construct quantum circuit architectures. Previous works have demonstrated that classical deep reinforcement learning (DRL) algorithms can successfully construct quantum circuit architectures without encoded physics knowledge. However, these DRL-based works are not generalizable to settings with changing device noises, thus requiring considerable amounts of training resources to keep the RL models up-to-date. With this in mind, we incorporated continual learning to enhance the performance of our algorithm. In this paper, we present the Probabilistic Policy Reuse with deep Q-learning (PPR-DQL) framework to tackle this circuit design challenge. By conducting numerical simulations over various noise patterns, we demonstrate that the RL agent with PPR was able to find the quantum gate sequence to generate the two-qubit Bell state faster than the agent that was trained from scratch. The proposed framework is general and can be applied to other quantum gate synthesis or control problems -- including the automatic calibration of quantum devices.
Assessing Post-editing Effort in the English-Hindi Direction

We present findings from a first in-depth post-editing effort estimation study in the English-Hindi direction along multiple effort indicators. We conduct a controlled experiment involving professional translators, who complete assigned tasks alternately, in a translation from scratch and a post-edit condition. We find that post-editing reduces translation time (by 63%), utilizes fewer keystrokes (by 59%), and decreases the number of pauses (by 63%) when compared to translating from scratch. We further verify the quality of translations thus produced via a human evaluation task in which we do not detect any discernible quality differences.
Unleashing the Power of Transformer for Graphs

Despite recent successes in natural language processing and computer vision, Transformer suffers from the scalability problem when dealing with graphs. The computational complexity is unacceptable for large-scale graphs, e.g., knowledge graphs. One solution is to consider only the near neighbors, which, however, will lose the key merit of Transformer to attend to the elements at any distance. In this paper, we propose a new Transformer architecture, named dual-encoding Transformer (DET). DET has a structural encoder to aggregate information from connected neighbors and a semantic encoder to focus on semantically useful distant nodes. In comparison with resorting to multi-hop neighbors, DET seeks the desired distant neighbors via self-supervised training. We further find these two encoders can be incorporated to boost each others' performance. Our experiments demonstrate DET has achieved superior performance compared to the respective state-of-the-art methods in dealing with molecules, networks and knowledge graphs with various sizes.
Stance Quantification: Definition of the Problem

Stance detection is commonly defined as the automatic process of determining the positions of text producers, towards a target. In this paper, we define a research problem closely related to stance detection, namely, stance quantification, for the first time. We define stance quantification on a pair including (1) a set of natural language text items and (2) a target. At the end of the stance quantification process, a triple is obtained which consists of the percentages of the number of text items classified as Favor, Against, Neither, respectively, towards the target in the input pair. Also defined in the current paper is a significant subproblem of the stance quantification problem, namely, multi-target stance quantification. We believe that stance quantification at the aggregate level can lead to fruitful results in many application settings, and furthermore, stance quantification might be the sole stance related analysis alternative in settings where privacy concerns prevent researchers from applying generic stance detection.