Reviewerly/exHarmony huggingface dataset:
Techno-Economic Analysis of Fixed, Single and Dual-Axis Tracking Solar PV System

The continuously increasing world electricity demand and environmental degradation due to fossil fuel sources are one the foremost problem to all the countries. Solar PV (photovoltaic) technology is emerging as a solution to mitigate the energy demand, energy availability and environmental degradation problems. It directly converts solar energy into electrical energy without any environmental concern. The output power capacity of the PV system largely depends upon solar radiation intensity. The diurnal and seasonal changing position of the sun concerning PV system location leads to variable solar radiation intensity at different times of the year. This problem can compensate using suntracking, which rotates the PV system such that maximum solar radiation received by the system irrespective of sun position. In this paper techno-economic analysis of fixed, single-axis (1-axis) tracking and dual-axis (2-axis) tracking solar PV system is presented. The comparative study is performed for the above-mentioned PV orientation techniques by considering 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> and 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> generation PV modules. The 76.8 kW grid-connected solar PV system is simulated by considering Mumbai’s meteorological conditions and real-time economic data.
Efficient Register Renaming Architectures for 8-bit AES Datapath at 0.55 pJ/bit in 16-nm FinFET

Small-footprint implementations of the advanced encryption standard (AES) algorithm are of interest in resource-constrained applications like Internet of Things (IoT). Symmetries in AES allow the datapath to be scaled down to the S-Box width of 8 bits, but the ShiftRows operation leads to a potential data hazard that must be avoided. The common method for resolving the ShiftRows hazard wastes power by moving data through a sequence of pipelined registers. We present in this article a novel 8-bit AES architecture that solves data movement inefficiencies by renaming registers and saves clock power with a single state update per AES round. We then extend register renaming to include microarchitectural randomization to mitigate susceptibility to side-channel attacks, which are a concern especially for low power implementations of AES. We fabricate and evaluate our designs in a commercial 16-nm FinFET technology. Testchip measurements show that the register renaming architecture encrypts data at 0.55 pJ/bit at nominal voltage, a 2.2× improvement over a state-of-the-art reference 8-bit design implemented in the same technology. Side-channel evaluation indicates that the randomized variant of register renaming significantly reduces vulnerability to differential power analysis (DPA).
Agreement and Disagreement between True and False-Positive Metrics in Recommender Systems Evaluation

False-positive metrics can capture an important side of recommendation quality, focusing on the impact of suggestions that are disliked by users, as a complement of common metrics that only measure the amount of successful recommendations. In this paper we research the extent to which false-positive metrics agree or disagree with true-positive metrics in the offline evaluation of recommender systems. We discover a surprising degree of systematic disagreement that was occasionally noted but not explained in the literature by previous authors. We find an explanation for the discrepancy be-tween the metrics in the effect of popularity biases, which impact false and true-positive metrics in very different ways: instead of rewarding the recommendation of popular items, as with true-positive, false-positive metrics penalize the popular. We determine precise conditions and cases in the general trends, with a formal explanation for our findings, which we confirm and illustrate empirically in experiments with different datasets.
Privacy Enhanced Decision Tree Inference

In many areas in machine learning, decision trees play a crucial role in classification and regression. When a decision tree based classifier is hosted as a service in a critical application with the need for privacy protection of the service as well as the user data, fully homomorphic encrypted can be employed. However, a decision node in a decision tree can't be directly implemented in FHE. In this paper, we describe an end-to-end approach to support privacyenhanced decision tree classification using IBM supported open-source library HELib. Using several options for building a decision node and employing oblivious computations coupled with an argmax function in FHE we show that a highly secure and trusted decision tree service can be enabled.
Sharing Incentive Mechanism, Task Assignment and Resource Allocation for Task Offloading in Vehicular Mobile Edge Computing

Vehicular Mobile Edge Computing is a promising technology to leverage the bottleneck at a base station (BS) at peak hours. However, to deploy Vehicular Mobile Edge Computing requires to deal with the challenges in how to incentive vehicles to resource sharing and how to assign tasks and computation resource to minimize the total network delay. In this paper, we develop a two-stage incentive mechanism and task assignment and resource allocation scheme by combining auction game, matching theory, and convex optimization method. In the first stage, we present the incentive problem between the BS and nearby vehicles, which leverages a reserve auction. Then we study the network delay minimization problem. The problem is decoupled into two subproblems for determining task assignment and computing resource allocation, respectively. Finally, numerical results show the effectiveness and efficiency of our scheme.
CLAREL: Classification via retrieval loss for zero-shot learning

We address the problem of learning cross-modal representations. We propose an instance-based deep metric learning approach in joint visual and textual space. The key novelty of this paper is that it shows that using per-image semantic supervision leads to substantial improvement in zero-shot performance over using class-only supervision. We also provide a probabilistic justification and empirical validation for a metric rescaling approach to balance the seen/unseen accuracy in the GZSL task. We evaluate our approach on two fine-grained zero-shot datasets: cub and flowers.
Decoupling Video and Human Motion: Towards Practical Event Detection in Athlete Recordings

In this paper we address the problem of motion event detection in athlete recordings from individual sports. In contrast to recent end-to-end approaches, we propose to use 2D human pose sequences as an intermediate representation that decouples human motion from the raw video information. Combined with domain-adapted athlete tracking, we describe two approaches to event detection on pose sequences and evaluate them in complementary domains: swimming and athletics. For swimming, we show how robust decision rules on pose statistics can detect different motion events during swim starts, with a F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score of over 91% despite limited data. For athletics, we use a convolutional sequence model to infer stride-related events in long and triple jump recordings, leading to highly accurate detections with 96% in F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score at only ±5ms temporal deviation. Our approach is not limited to these domains and shows the flexibility of pose-based motion event detection.
A Multimodal Fusion Framework for Brand Recognition from Product Image and Context

The detection and recognition of a brand from product im-ages is a key capability in many computer vision and machine learning applications. Specifically, logo detection from image is one of the most distinctive and effective ways to determine the brand. However, due to the large variation in scale, geometry and appearance, etc., logo detection and recognition remains a challenging problem, even with the recent advances using deep neural networks. Another informative source for brand recognition is the textual information within the context as in most of the e-commerce websites, a product picture is often accompanied by some text description as well. To combine the best of both worlds, we propose to tackle brand recognition with a multimodal fusion framework that integrates image-based logo recognition using convolutional neural networks with context feature (product image title, description, OCR text detection from image, etc.)-based brand recognition using natural language understanding models. We demonstrated experimentally that the additional context information has significantly mitigated the limitations experienced by image-only-based logo recognition. It is worth noting that, in order to better represent text within its context, we have adopted the texts embedded using BERT in our framework.
TOWARD A COMPREHENSIVE DEFINITION OF THE NON-USER

Abstract The user has been very well defined over the last decades. With human-centered design becoming more widely applied within various industries, the user's needs are being taken into account more than ever. What is often overlooked is the user's counterpart: the non-user. Integrating the non-user into modern development projects provides great additional value. This paper is compiling current definitions in order to analyse them within the context of product development and to make a contribution toward a comprehensive definition of the non-user that can be applied to various disciplines.
On the limits of forgetting in Answer Set Programming

Selectively forgetting information while preserving what matters the most is becoming an increasingly important issue in many areas, including in knowledge representation and reasoning. Depending on the application at hand, forgetting operators are defined to obey different sets of desirable properties. It turns out that, of the myriad of desirable properties discussed in the context of forgetting in Answer Set Programming, strong persistence, which imposes certain conditions on the correspondence between the answer sets of the program pre- and post-forgetting, and a certain independence from non-forgotten atoms, seems to best capture its essence, and be desirable in general. However, it has remained an open problem whether it is always possible to forget a set of atoms from a program while obeying strong persistence. In this paper, we investigate the limits of forgetting in Answer Set Programming. After showing that it is not always possible to forget a set of atoms from a program while obeying this property, we move forward and precisely characterize what can and cannot be forgotten from a program, by presenting a necessary and sufficient criterion. This characterization allows us to draw some important conclusions regarding the existence of forgetting operators for specific classes of logic programs, to characterize the class of forgetting operators that achieve the correct result whenever forgetting is possible, and investigate the related question of determining what we can forget from some specific logic program. Subsequently, we address the issue of what to do when we must forget a set of atoms, but cannot without violating this property. To this end, we investigate three natural alternatives to forget when forgetting without violating strong persistence is not possible, which turn out to correspond to the different natural possible relaxations of the characterization of strong persistence. Additionally, before concluding, we address computational complexity issues – namely of checking whether the novel criterion holds and whether a certain program is a result according to the different classes of forgetting operators we introduce – and discuss the related literature.
ECBCM: A prestige‐based edge computing blockchain security consensus model

Abstract The explosive growth of data in the network has brought huge burdens and challenges to traditional centralized cloud computing data processing. To solve this problem, edge computing technology came into being. Because the edge is closer to the user, processing part of the data at the edge can also bring a faster response to the user and improve their experience. However, the existing edge computing platforms have problems such as data storage security and multiparty data mutual trust. Blockchain technology has become an important means to solve the above data storage and sharing problems due to its excellent characteristics. The core of blockchain technology is consensus, and its speed and security will directly affect the efficiency and stability of the blockchain system. Therefore, this study uses the consensus mechanism as an entry point to reduce the resource consumption of the edge computing blockchain system and improve its security. In order to reduce the resource consumption of traditional consensus algorithms, improve their adaptability in the edge computing environment, and solve the security problem caused by the concentration of node rights, a prestige‐based edge computing blockchain security consensus model (ECBCM) is proposed. ECBCM is a general model based on prestige rewards and penalties. It also introduces a node replacement mechanism to ensure the fault tolerance of the consensus process. According to the results of multiple sets of performance comparison experiments and security verification experiments after embedding the existing consensus algorithm, the validity of the consensus model is confirmed.
Online Speech Recognition Using Multichannel Parallel Acoustic Score Computation and Deep Neural Network (DNN)- Based Voice-Activity Detector

This paper aims to design an online, low-latency, and high-performance speech recognition system using a bidirectional long short-term memory (BLSTM) acoustic model. To achieve this, we adopt a server-client model and a context-sensitive-chunk-based approach. The speech recognition server manages a main thread and a decoder thread for each client and one worker thread. The main thread communicates with the connected client, extracts speech features, and buffers the features. The decoder thread performs speech recognition, including the proposed multichannel parallel acoustic score computation of a BLSTM acoustic model, the proposed deep neural network-based voice activity detector, and Viterbi decoding. The proposed acoustic score computation method estimates the acoustic scores of a context-sensitive-chunk BLSTM acoustic model for the batched speech features from concurrent clients, using the worker thread. The proposed deep neural network-based voice activity detector detects short pauses in the utterance to reduce response latency, while the user utters long sentences. From the experiments of Korean speech recognition, the number of concurrent clients is increased from 22 to 44 using the proposed acoustic score computation. When combined with the frame skipping method, the number is further increased up to 59 clients with a small accuracy degradation. Moreover, the average user-perceived latency is reduced from 11.71 s to 3.09–5.41 s by using the proposed deep neural network-based voice activity detector.
Nonparametric Object and Parts Modeling With Lie Group Dynamics

Articulated motion analysis often utilizes strong prior knowledge such as a known or trained parts model for humans. Yet, the world contains a variety of articulating objects--mammals, insects, mechanized structures--where the number and configuration of parts for a particular object is unknown in advance. Here, we relax such strong assumptions via an unsupervised, Bayesian nonparametric parts model that infers an unknown number of parts with motions coupled by a body dynamic and parameterized by SE(D), the Lie group of rigid transformations. We derive an inference procedure that utilizes short observation sequences (image, depth, point cloud or mesh) of an object in motion without need for markers or learned body models. Efficient Gibbs decompositions for inference over distributions on SE(D) demonstrate robust part decompositions of moving objects under both 3D and 2D observation models. The inferred representation permits novel analysis, such as object segmentation by relative part motion, and transfers to new observations of the same object type.
SQE: a Self Quality Evaluation Metric for Parameters Optimization in Multi-Object Tracking

We present a novel self quality evaluation metric SQE for parameters optimization in the challenging yet critical multi-object tracking task. Current evaluation metrics all require annotated ground truth, thus will fail in the test environment and realistic circumstances prohibiting further optimization after training. By contrast, our metric reflects the internal characteristics of trajectory hypotheses and measures tracking performance without ground truth. We demonstrate that trajectories with different qualities exhibit different single or multiple peaks over feature distance distribution, inspiring us to design a simple yet effective method to assess the quality of trajectories using a two-class Gaussian mixture model. Experiments mainly on MOT16 Challenge data sets verify the effectiveness of our method in both correlating with existing metrics and enabling parameters self-optimization to achieve better performance. We believe that our conclusions and method are inspiring for future multi-object tracking in practice.
Secure Fast Covariance Intersection Using Partially Homomorphic and Order Revealing Encryption Schemes

Fast covariance intersection is a widespread technique for state estimate fusion in sensor networks when cross-correlations are not known and fast computations are desired. The common requirement of sending estimates from one party to another during fusion forfeits local privacy. Current secure fusion algorithms rely on encryption schemes that do not provide sufficient flexibility. As a result, excess communication between estimate producers is required, which is often undesirable. We propose a novel method of homomorphically computing the fast covariance intersection algorithm on estimates encrypted with a combination of encryption schemes. Using order revealing encryption, we show how an approximate solution to the fast covariance intersection weights can be computed and combined with partially homomorphic encryptions of estimates, to calculate an encryption of the fused result. The described approach allows secure fusion of any number of private estimates, making third-party cloud processing a viable option when working with sensitive state estimates or when performing estimation over untrusted networks.
Fine-grained assured data deletion scheme based on attribute association

With the rapid development of cloud computing technology, an increasing number of enterprises and users store their data in the cloud to achieve convenient storage and data sharing. However, once data is stored in a third-party cloud storage service provider, the privacy and integrity of the data can be compromised. This paper proposes a fine-grained assured data deletion scheme based on attribute association (ADAA), which aims to protect security and achieve assured deletion of outsourced data. The scheme uses standard encryption technology to ensure the privacy and integrity of the data and implements secure deletion based on attribute revocation. At the same time, it incorporates access policy graphs and policy combinations to achieve fine-grained access control and data sharing. In addition, it associates attributes to achieve access control by using a homomorphic hash function, and it establishes a deletion confirmation feedback mechanism to provide a reliable guarantee. By experimenting and comparing with related schemes, ADAA is proved to be safe and efficient, and it provides a practical method for the assured deletion of cloud storage data.
Hybrid Approach to Face Recognition System using PCA &amp; LDA in Border Control

In this paper, we have proposed a hybrid approach to face recognition using PCA and LDA in border control like time-critical application. Duplicate passports, unauthorized VISA, fake identities, and border criminals have dramatically increased within the last few years in Sri Lanka due to a lack of proper identification system at the borders. Even though in the traditional VISA granting process biometrics are sent to borders and final destinations such as immigration counters, there are no proper face recognition measures at airports, to prove that it is the same person who comes to apply VISA that is identified at the airport. The proposed system uses Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) to compare updated face biometrics and the physical appearance at the airport in a novel hybrid way. After the face recognition is done by a hybrid solution with a confidence level, the candidate will proceed to the manual process as usual. The proposed system speeds up the traditional process, and increasing the accuracy of identification and smooth adaptation to the traditional method can be identified as the main benefits of the system. Face biometrics is the main ingredient of the proposed system. The system has been analysed with the traditional model and evaluated with authentic biometric sample and identified with 98% accuracy in face recognition with less average time. A nearest mean hybrid approach in the time-critical application can be identified as the novelty of the proposed system.
Regular language type inference with term rewriting

This paper defines a new type system applied to the fully automatic verification of safety properties of tree-processing higher-order functional programs. We use term rewriting systems to model the program and its semantics and tree automata to model algebraic data types. We define the regular abstract interpretation of the input term rewriting system where the abstract domain is a set of regular languages. From the regular abstract interpretation we derive a type system where each type is a regular language. We define an inference procedure for this type system which allows us check the validity of safety properties. The inference mechanism is built on an invariant learning procedure based on the tree automata completion algorithm. This invariant learning procedure is regularly-complete and complete in refutation, meaning that if it is possible to give a regular type to a term then we will eventually find it, and if there is no possible type (regular or not) then we will eventually find a counter-example.