Reviewerly/exHarmony huggingface dataset:
CBDC: Banking and Anonymity

What is the optimal design of anonymity in a central bank digital currency (CBDC)? We examine this question in the context of bank lending by building a stylized model of anonymity in payment instruments. We specify the anonymity of payment instruments in two dimensions: The bank has no information about the entrepreneur's investment, and the bank has less control over the entrepreneur's profits. An instrument with higher anonymity may discourage the bank from lending, and thus, the entrepreneur strategically chooses payment instruments. Our analysis shows that introducing a CBDC with modest anonymity can improve welfare in one equilibrium, but can also destroy valuable information in bank lending, leading to inefficient lending in another equilibrium. Our results suggest that central banks should either make a CBDC highly anonymous or share CBDC data with banks to eliminate this bad equilibrium.
EVStore: Storage and Caching Capabilities for Scaling Embedding Tables in Deep Recommendation Systems

Modern recommendation systems, primarily driven by deep-learning models, depend on fast model inferences to be useful. To tackle the sparsity in the input space, particularly for categorical variables, such inferences are made by storing increasingly large embedding vector (EV) tables in memory. A core challenge is that the inference operation has an all-or-nothing property: each inference requires multiple EV table lookups, but if any memory access is slow, the whole inference request is slow. In our paper, we design, implement and evaluate EVStore, a 3-layer EV table lookup system that harnesses both structural regularity in inference operations and domain-specific approximations to provide optimized caching, yielding up to 23% and 27% reduction on the average and p90 latency while quadrupling throughput at 0.2% loss in accuracy. Finally, we show that at a minor cost of accuracy, EVStore can reduce the Deep Recommendation System (DRS) memory usage by up to 94%, yielding potentially enormous savings for these costly, pervasive systems.
Stability Control of Position Flow Fuzzy Estimation in Swarm Intelligence Aware Privacy Protection

The group intelligence perception privacy protection model is a method to achieve the balance between user privacy and service requests through the cooperation between users using location services and has a good perception effect. In order to better protect the location privacy of network users and improve the stability control effect of fuzzy estimation of location flow, this paper designs a stability control method of fuzzy estimation of location flow in group intelligent perception privacy protection. This method uses the group intelligence aware privacy protection model to obtain the user network location coordinates in the group intelligence aware privacy protection. Taking the user’s network location coordinates as input, the location flow queue of multiple users in the group intelligence aware privacy protection network is obtained by the Lyapunov multiobjective location flow estimation queue model. After the fuzzy processing of the user location flow queue, the online control mechanism of location flow fuzzy estimation stability under different conditions is established. According to the online control mechanism, a stability control method based on access control and group intelligence aware task allocation is used to realize the stability control of location flow fuzzy estimation in group intelligence aware privacy protection. The experimental results show that the method can obtain 100% of the user location integrity in the group intelligence aware privacy protection, and the target location flow estimation queue is more accurate. It can effectively reduce the number of communication rounds of fuzzy estimation of location flow in the group intelligence aware privacy protection and has better stability control ability.
Decker: Attack Surface Reduction via On-Demand Code Mapping

Modern code reuse attacks take full advantage of bloated software. Attackers piece together short sequences of instructions in otherwise benign code to carry out malicious actions. Mitigating these reusable code snippets, known as gadgets, has become one of the prime focuses of attack surface reduction research. While some debloating techniques remove parts of software that contain such gadgets, other methods focus on making them unusable by breaking up chains of them, thereby substantially diminishing the possibility of code reuse attacks. Third-party libraries are another main focus, because they exhibit a high number of vulnerabilities, but recently, techniques have emerged that deal with whole applications. Attack surface reduction efforts have typically tried to eliminate such attacks by subsetting (debloating) the application, e.g. via user-specified inputs, configurations, or features to achieve high gadget reductions. However, such techniques suffer from the limitations of soundness, i.e. the software might crash during no-attack executions on regular inputs, or they may be conservative and leave a large amount of attack surface untackled.
Compilation Consistency Modulo Debug Information

Compilation Consistency Modulo Debug Information (CCMD) is an essential compiler property that a production compiler should support: the compiler should emit the same machine code regardless of enabling debug information. CCMD is vital to developers' experiences with debugging a production binary containing no debug information. To debug such a binary, developers need build another binary with the same compiler flags and enable debug information. Without CCMD, the machine code in the latter binary will be different, which can confuse the debugger, hide the bug, or even cause a miscompilation (as GCC once did with the Linux Kernel).
Khuzdul: Efficient and Scalable Distributed Graph Pattern Mining Engine

This paper proposes Khuzdul, a distributed execution engine with a well-defined abstraction that can be integrated with existing single-machine graph pattern mining (GPM) systems to provide efficiency and scalability at the same time. The key novelty is the extendable embedding abstraction which can express pattern enumeration algorithms, allow fine-grained task scheduling, and enable low-cost GPM-specific data reuse to reduce communication cost. The effective BFS-DFS hybrid exploration generates sufficient concurrent tasks for communication-computation overlapping with bounded memory consumption. Two scalable distributed GPM systems are implemented by porting Automine and GraphPi on Khuzdul. Our evaluation shows that Khuzdul based systems significantly outperform state-of-the-art distributed GPM systems with partitioned graphs by up to 75.5× (on average 19.0×), achieve similar or even better performance compared with the fastest distributed GPM systems with replicated graph, and scale to massive graphs with more than one hundred billion edges with a commodity cluster.
Achieving Sub-second Pairwise Query over Evolving Graphs

Many real-time OLAP systems have been proposed to query evolving data with sub-second latency. Although this feature is highly attractive, it is very hard to be achieved on analytic graph queries that can only be answered after accessing every connected vertex. Fortunately, researchers recently observed that answering pairwise queries is enough for many real-world scenarios. These pairwise queries avoid the exhaustive nature and hence may only need to access a small portion of the graph. Obviously, the crux of achieving low latency is to what extent the system can eliminate unnecessary computations. This pruning process, according to our investigation, is usually achieved by estimating certain upper bounds of the query result in existing systems.
Copy-on-Pin: The Missing Piece for Correct Copy-on-Write

Operating systems utilize Copy-on-Write (COW) to conserve memory and improve performance. During the last two decades, a series of COW-related bugs - which compromised security, corrupted memory and degraded performance - was found. The majority of these bugs are related to page "pinning", which operating systems employ to access process memory efficiently and to perform direct I/O. Unfortunately, the true cause of these bugs is not well understood, resulting in incomplete bug fixes. We show this by: (1) surveying previously reported pinning-related COW bugs; (2) uncovering new such bugs in Linux, FreeBSD, and NetBSD; and (3) showing that they occur because the COW logic does not consider page pinnings correctly, resulting in incorrect behavior (e.g., I/O of stale data). We then address the underlying problem by deriving when/how shared pages must be copied and under which conditions pinned pages can be shared to maintain correctness. Based on this assessment, we introduce the "Copy-on-Pin (COP)" scheme, an extension of the COW mechanism that handles pinned pages correctly by ensuring pinned pages and shared pages are mutually exclusive. However, we find that a naive implementation of this scheme hampers performance and increases complexity if pages are copied only when strictly necessary. To compensate, we introduce a relaxed-COP design, which does not require precise tracking of page sharing, maintains correctness without increasing complexity, and (while potentially needlessly copying pages in some corner cases) marginally improves performance. Our relaxed-COP solution has been integrated into Linux 5.19.
Deep Learning based Wireless Channel Prediction: 5G Scenario

In the area of wireless communication, channel estimation is a challenging problem due to the need for real-time implementation as well as system dependence on the estimation accuracy. This work presents a Long-Short Term Memory (LSTM) based deep learning (DL) approach for the prediction of channel response in real-time and real-world non-stationary channels. The model uses the pre-defined history of channel impulse response (CIR) data along with two other features viz. transmitter-receiver update distance and root-mean-square delay spread values which are also changing in time with the channel impulse response. The objective is to obtain an approximate estimate of CIRs using prediction through the DL model as compared to conventional methods. For training the model, a sample dataset is generated through the open-source channel simulation software NYUSIM which realizes samples of CIRs for measurement-based channel models based on various multipath channel parameters. From the model test results, it is found that the proposed DL approach provides a viable lightweight solution for channel prediction in wireless communication.
Data protection governance framework: A silver bullet for blockchain-enabled applications

Blockchain technology is taking centre stage in major industries, ushering in a new era of decentralisation and digitalisation. While blockchain has garnered widespread traction in various sectors, there remain many technological, operational, societal, and legal challenges in deploying blockchain, mainly attributable to its sheer novelty. With ensuing ruminations about data protection concerns and given the real threats posed by the increasing usage of blockchain, scholars have examined these underlying challenges faced by blockchain users and regulators globally. Trust-building issues, such as data protection breaches, warrant our attention due to the negative consequences that may manifest and concretise in any measurable manner, triggering fragmentation of blockchain-based applications and socio-technical assemblages. This paper proposes eight data protection indicators (DPIs) to assess the maturity levels of countries in addressing data protection challenges in the blockchain landscape. The DPIs can contribute to developing institutional and governance frameworks to spur the global diffusion of hard and soft law instruments and establish broader safeguards of blockchain users’ data.
A Novel Extreme Value Theory Based approach to Hyperparameter Optimization

Finding the optimal values of hyperparameters is crucial for many machine learning algorithms. Algorithms like deep learning, XGBoost, etc. require us to choose among a large set of hyperparameters, thus increasing the computational requirements for a successful hyperparameter search. Another factor complicating the hyperparameter search process is the variability in performance due to choice of different initial random seeds. This variability might be caused by different initial weight assignments or by different environment interactions in the training process. In this paper an extreme value theory (EVT) based approach for hyperparameter optimization in this noisy setting is proposed. To the best of our knowledge this is the first work based on EVT for hyperparameter optimization. It is demonstrated through extensive experimentation on many real world datasets (including a medical diabetes dataset consisting of several medical predictor variables), that by using only a fraction of observations near optimal hyperparameter values can be obtained using the parameters of a generalized extreme value distribution for the test set performance metric of interest in the algorithm.
A Model to Identify Redundancy and Relevancy in Question-Answer Systems of Digital Scholarly Platforms

Many digital scholarly platforms allow researchers to conduct scientific discussions using their question-answer systems. Such discussions are helpful for emerging as well as established researchers in numerous prospects. In order to rely on such discussions, it is essential to effectively validate these questions and answers based on content as well as context. This will help in finding ‘quality’ content, improving user experience for researchers and acquiring efficient results in realistic applications such as influence analysis, topic modeling, expert finding and recommendation systems. In this research, one of the well-known scholarly platforms, ResearchGate (RG) is focused. Questions and answers of 14,000 researchers from RG are rendered. A qualitative analysis is performed to perceive various anomalies in the rendered data. To resolve these anomalies, we propose a Word2Vec based model that identifies redundancy and relevancy in questions and answers using content as well as context analysis. The proposed model is evaluated on the rendered data and the outcome suggests that the model accurately identifies redundant questions and irrelevant answers from scholarly platforms.
Mining and searching app reviews for requirements engineering: Evaluation and replication studies

App reviews provide a rich source of feature-related information that can support requirement engineering activities. Analyzing them manually to find this information, however, is challenging due to their large quantity and noisy nature. To overcome the problem, automated approaches have been proposed for 'feature-specific analysis'. Unfortunately, the effectiveness of these approaches has been evaluated using different methods and datasets. Replicating these studies to confirm their results and to provide benchmarks of different approaches is a challenging problem. We address the problem by extending previous evaluations and performing a comparison of these approaches. In this paper, we present two empirical studies. In the first study, we evaluate opinion mining approaches; the approaches extract features discussed in app reviews and identify their associated sentiments. In the second study, we evaluate approaches searching for feature-related reviews. The approaches search for users' feedback pertinent to a particular feature. The results of both studies show these approaches achieve lower effectiveness than reported originally, and raise an important question about their practical use.
A lightweight model for blade tip image enhancement in helicopter rotor motion parameter measurement system

The helicopter rotor torsion angle is an important motion parameter of a helicopter rotor, which is helpful for the design of helicopter rotor system. The stereoscopic vision-based method measures the torsion angle by computing the directions of the blade tips in the blade tip images. However, the captured blade tip images suffer from blurring and underexposure, which affect the measurement accuracy. To this end, a lightweight deep model is proposed to improve the image quality. In addition, a blade tip image dataset is proposed to train the network. Corresponding experiments demonstrate the effectiveness of the proposed model. Compared to competitive methods, our model stands out with fewer parameters and faster inference time.
Learning cost action planning models with perfect precision via constraint propagation

Data-driven AI is rapidly gaining importance. In the context of AI planning, a constraint programming formulation for learning action models in a data-driven fashion is proposed. Data comprises plan observations, which are automatically transformed into a set of planning constraints which need to be satisfied. The formulation captures the essence of the action model and unifies functionalities that are individually supported by other learning approaches, such as costs, noise/uncertainty on actions, information on intermediate state observations and mutex reasoning. Reliability is a key concern in data-driven learning, but existing approaches usually learn action models that can be imprecise, where imprecision here is an error indicator of learning something incorrect. On the contrary, the proposed approach guarantees reliability in terms of perfect precision by using constraint propagation. This means that what is learned is 100% correct (i.e., error-free), not only for the initial observations, but also for future observations. To our knowledge, this is a novelty in action model learning literature. Although perfect precision might potentially limit the amount of learned information, the exhaustive experiments over 20 planning domains show that such amount is comparable, and even better, to ARMS and FAMA, two state-of-the-art benchmarks in action model learning.
A three-stage neural model for Arabic Dialect Identification

The Arabic language has several dialects across the twenty-two Arabic-speaking countries in Asia and Africa. Arabic Dialect Identification (ADI) is still a challenging task due to the well-recognized complexity and variations of Arabic dialects. It is noteworthy that Arabic dialects share the majority of tokens. The state-of-the-art solutions have been built upon various machine learning approaches. However, they commonly treat all words equally-likely and thus ignores the importance of dialectal words in response to a given dialect. In this paper, we propose a three-stage neural approach to learn the dialectal semantic representation from a given corpus. Specifically, we first aim to capture the dialect-relevant information, which is then used to model the dialectal vector representation. The goal is to filter away the shared words between dialects to reduce the noisy information fused to the fully connected layer. We introduce two variants, including LSTM-based and Transformer-based. Finally, we empirically evaluate the performance of the proposed solution by a comparative study on real benchmark datasets, including MADAR, NADI, and QADI. Our extensive experiments show that it consistently achieves state-of-the-art performance. Due to the well-recognized challenging of ADI, the improvement margins can be deemed considerable. The code is available on GitHub.1
Decision Making System Using Fuzzy Mamdani in Detecting Cholestrol Disease

Cholestrol is a physical property of natural substances in the form of fat which is useful as an essential building material for the body for the synthesis of important substances such as cell membranes and insulation materials around nerve fibers. In addition, it is useful for sex hormones, kidney children and bile acids. High cholesterol levels or hypercholesterolemia in the blood triggers hypertension. This is because high cholesterol is the cause of blockages in peripheral blood vessels that reduce blood supply to the heart. Based on these studies, it shows that the influence of obesity (overweight) and blood pressure has a role in the risk of cholesterol disease. Based on the rule view, it can be seen that the cholesterol level is 210 or it can be said that it is in the alert category, it can be seen for blood pressure 100.6 and BMI 31.8.The fuzzy mamdani method is often also known as the min-max method. Where it uses min or minimum on the implication function and max or maximum on the composition between implication functions. In its application, the madani fuzzy method uses 4 stages, namely the formation of fuzzy sets, application of implication functions, rule composition, defuzzification. This research uses a simulation method of calculating cholesterol. This research analyzes using fuzzy mamdani and matlab software assistance.
CRITICAL ANALYSIS OF INDIAN ECONOMY ON THE BASIS OF ₹5 COMMEMORATIVE COINS

The government of India is issuing a series of new coins every year, which make up a valuable aspect of the Indian currency system. Reserve Bank of India is the sole authority to designing and minting of various denominations. These coins are minted at the four Indian Government Mints at Noida (UP), Alipore (Kolkata), Cherlapally (Hyderabad), and Mumbai. Government of India issues commemorative coins to check event of incredible characters with a durable, remarkable, and exceptional contribution towards society, and so forth and to recall occasions which had extraordinary historical importance. The present study focused on the historical perspective of ₹5 commemorative coins, which was first time issued in the commemoration of Indra Gandhi. Furthermore I have examined on composition of metals, and critical analysis of these commemorative coins based on metal, weight, diameter and thickness.
Lightweight vehicle object detection network for unmanned aerial vehicles aerial images

Due to the limited computing power of unmanned aerial vehicles (UAVs) and the problems of missed detection and wrong detection of small objects, the current object detection algorithm cannot achieve real-time and high-precision detection. To solve these problems, we propose a vehicle detection network Shuffle CarNet for UAVs aerial images, which is composed of a feature extraction network, a feature fusion network, and a three-scale prediction network. First, according to the limited hardware resources of embedded devices, a lightweight feature extraction network Light CarNet is proposed by fusing the attention mechanism. Second, a four-scale feature bidirectional weighted fusion module is designed. According to the characteristics of the object scale, multilevel feature map bidirectional weighted fusion is selected for target classification and bounding box regression on three scales. Finally, Car-non-maximum suppression is used to reduce false detection and missed detection. Experiments show that compared with other algorithms on the VisDrone-2019 dataset, the proposed method improves the mean average precision by 1.14%, achieves a precision of 82.96%, and can meet the needs of real-time vehicle detection. The superiority of this method is proved by many comparative experiments.
Consciousness as a Trigger to Adaptation

Consciousness refers to mental states of a cognitive agent that make it aware of elements of its environment and its own state. Phenomena of consciousness have been studied in Philosophy, Psychology and Cognitive Science. With the advent of Artificial Intelligence (AI), the study of consciousness has broadened to include questions such as “When is an AI system conscious?”, “What role does consciousness play in the cognitive architecture of an AI system?”. This paper proposes a cognitive architecture that attempts to answer such questions and is an extension of proposals for practical reasoning, founded on the notion of epistemic goals, goals where the desired state-of-affairs is knowledge of something in the environment or within the agent itself. The proposal is illustrated with an example involving an autonomous vehicle.