Reviewerly/exHarmony huggingface dataset:
Compilable Neural Code Generation with Compiler Feedback

Automatically generating compilable programs with (or without) natural language descriptions has always been a touchstone problem for computational linguistics and automated software engineering.Existing deep-learning approaches model code generation as text generation, either constrained by grammar structures in decoder, or driven by pre-trained language models on large-scale code corpus (e.g., CodeGPT, PLBART, and CodeT5).However, few of them account for compilability of the generated programs.To improve compilability of the generated programs, this paper proposes COMPCODER, a three-stage pipeline utilizing compiler feedback for compilable code generation, including language model fine-tuning, compilability reinforcement, and compilability discrimination.Comprehensive experiments on two code generation tasks demonstrate the effectiveness of our proposed approach, improving the success rate of compilation from 44.18 to 89.18 in code completion on average and from 70.3 to 96.2 in text-to-code generation, respectively, when comparing with the state-ofthe-art CodeGPT.
Detecting Human-Object Interactions with Object-Guided Cross-Modal Calibrated Semantics

Human-Object Interaction (HOI) detection is an essential task to understand human-centric images from a fine-grained perspective. Although end-to-end HOI detection models thrive, their paradigm of parallel human/object detection and verb class prediction loses two-stage methods' merit: object-guided hierarchy. The object in one HOI triplet gives direct clues to the verb to be predicted. In this paper, we aim to boost end-to-end models with object-guided statistical priors. Specifically, We propose to utilize a Verb Semantic Model (VSM) and use semantic aggregation to profit from this object-guided hierarchy. Similarity KL (SKL) loss is proposed to optimize VSM to align with the HOI dataset's priors. To overcome the static semantic embedding problem, we propose to generate cross-modality-aware visual and semantic features by Cross-Modal Calibration (CMC). The above modules combined composes Object-guided Cross-modal Calibration Network (OCN). Experiments conducted on two popular HOI detection benchmarks demonstrate the significance of incorporating the statistical prior knowledge and produce state-of-the-art performances. More detailed analysis indicates proposed modules serve as a stronger verb predictor and a more superior method of utilizing prior knowledge. The codes are available at https://github.com/JacobYuan7/OCN-HOI-Benchmark.
Application of Deep Learning to Construct Breast Cancer Diagnosis Model

(1) Background: According to Taiwan’s ministry of health statistics, the rate of breast cancer in women is increasing annually. Each year, more than 10,000 women suffer from breast cancer, and over 2000 die of the disease. The mortality rate is annually increasing, but if breast cancer tumors are detected earlier, and appropriate treatment is provided immediately, the survival rate of patients will increase enormously. (2) Methods: This research aimed to develop a stepwise breast cancer model architecture to improve diagnostic accuracy and reduce the misdiagnosis rate of breast cancer. In the first stage, a breast cancer risk factor dataset was utilized. After pre-processing, Artificial Neural Network (ANN) and the support vector machine (SVM) were applied to the dataset to classify breast cancer tumors and compare their performances. The ANN achieved 76.6% classification accuracy, and the SVM using radial functions achieved the best classification accuracy of 91.6%. Therefore, SVM was utilized in the determination of results concerning the relevant breast cancer risk factors. In the second stage, we trained AlexNet, ResNet101, and InceptionV3 networks using transfer learning. The networks were studied using Adaptive Moment Estimation (ADAM) and Stochastic Gradient Descent with Momentum (SGDM) based optimization algorithm to diagnose benign and malignant tumors, and the results were evaluated; (3) Results: According to the results, AlexNet obtained 81.16%, ResNet101 85.51%, and InceptionV3 achieved a remarkable accuracy of 91.3%. The results of the three models were utilized in establishing a voting combination, and the soft-voting method was applied to average the prediction result for which a test accuracy of 94.20% was obtained; (4) Conclusions: Despite the small number of images in this study, the accuracy is higher compared to other literature. The proposed method has demonstrated the need for an additional productive tool in clinical settings when radiologists are evaluating mammography images of patients.
Unified Semantic Typing with Meaningful Label Inference

Semantic typing aims at classifying tokens or spans of interest in a textual context into semantic categories such as relations, entity types, and event types.The inferred labels of semantic categories meaningfully interpret how machines understand components of text.In this paper, we present UNIST, a unified framework for semantic typing that captures label semantics by projecting both inputs and labels into a joint semantic embedding space.To formulate different lexical and relational semantic typing tasks as a unified task, we incorporate task descriptions to be jointly encoded with the input, allowing UNIST to be adapted to different tasks without introducing task-specific model components.UNIST optimizes a margin ranking loss such that the semantic relatedness of the input and labels is reflected from their embedding similarity.Our experiments demonstrate that UNIST achieves strong performance across three semantic typing tasks: entity typing, relation classification and event typing.Meanwhile, UNIST effectively transfers semantic knowledge of labels and substantially improves generalizability on inferring rarely seen and unseen types.In addition, multiple semantic typing tasks can be jointly trained within the unified framework, leading to a single compact multi-tasking model that performs comparably to dedicated single-task models, while offering even better transferability.
Multi-scale band-limited illumination profilometry for robust three-dimensional surface imaging at video rate

Dynamic three-dimensional (3D) surface imaging by phase-shifting fringe projection profilometry has been widely implemented in diverse applications. However, existing techniques fall short in simultaneously providing the robustness in solving spatially isolated 3D objects, the tolerance of large variation in surface reflectance, and the flexibility of tunable working distances with meter-square-level fields of view (FOVs) at video rate. In this work, we overcome these limitations by developing multi-scale band-limited illumination profilometry (MS-BLIP). Supported by the synergy of dual-level intensity projection, multi-frequency fringe projection, and an iterative method for distortion compensation, MS-BLIP can accurately discern spatially separated 3D objects with highly varying reflectance. MS-BLIP is demonstrated by dynamic 3D imaging of a translating engineered box and a rotating vase. With an FOV of up to 1.7 m × 1.1 m and a working distance of up to 2.8 m, MS-BLIP is applied to capturing full human-body movements at video rate.
Assessing Adoption and Implementation of Mobile Technology-Based Library Services in Academic Libraries

Mobile Technologies (m-tech) have been embraced by stakeholders of academic libraries globally in recent times as a medium that can offer convenient library services to library patrons. This study was to ascertain the potentials of adopting and implementing m-tech based library services in academic libraries in Ghana. The study was a descriptive survey and the mixed-method approach was used. The major findings were that there were a strong awareness and deep appreciation for the use of m-tech library services among library management and students, however, m-tech based library services have not been implemented in the libraries due to diverse reasons. These include inadequate ICT infrastructure, skills gap as a result of lack of training, lack of policy framework for the adoption of the technology and clearing the hurdle of convincing university management to accept the technology. As a result, it was recommended that the libraries need to invest more in ICT infrastructure specifically m-tech infrastructure and build on their human resource base through recruitment and professional training on emerging technologies
Scalable information retrieval system in semantic web by query expansion and ontological-based LSA ranking similarity measurement

In recent days, semantic web presents a key role in intelligent retrieval of information system that resolves vocabulary mismatch problem by query expansion process. However, achieving the scalable information retrieval (IR) in semantic web is a challenging issue in a large dataset. The semantic IR problem is addressed by an ontological-based semantic similarity measurement using natural language processing. The two novel algorithms namely syntactic correlation coefficient (SCC) and mapping-based K-nearest neighbour (M-KNN) for semantic similarity measurement is proposed which improves the accuracy of relevant result. The ontological constructs with word sense disambiguation (WSD) algorithm for document repository improves the conceptual relationships, reduces the ambiguities in ontology and improves scalability by intensely analysing the semantic relationship as well as dynamically reconstructing the ontology when numbers of documents are updated. Ranking is done with latent semantic analysis (LSA) after semantic similarity analysis, which improves the retrieved result and reduces the complexity in relevancy. The performance of the system is analysed with respect to different metrics such as processing time, F-measure (0.97), time complexity, precision (0.95), recall (0.98) and space complexity.
Mining Social Media Content to Predict Peer Trust Level in Social Networks

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NLP based Extraction of Relevant Resume using Machine Learning

Today, the proportion of bits of knowledge making is incredibly tremendous. Dependent upon the adjustments of estimations, immense information involves social Data, machine data, and trade-based Data. Social estimations gathered from Facebook, Twitter, etc. Machine information is RFID chip examining, GPRS, etc. Trade based bits of knowledge consolidate retail site's information. Around the assortments of different sorts of estimations first segment is printed content real factors. Content information is sorted out information. Deriving of high five star sorted out records from the unstructured printed content is artistic substance examination. Changing over unstructured real factors into critical records is a book assessment process.CV parsing is one of the substance examination strategies. It is keep parsing or extraction of CV.CV parser combines the candidate's resume with selection gems flow and thusly systems moving toward CV's. This paper proposes a CV parser adjustment of the usage of artistic substance examination. The proposed CV parser interpretation isolates substances required in the enlistment methodology inside the associations
Presence of base metals in the southern extension of Zawarmala Dolomite, Udaipur, Rajasthan, India

This communication reports the presence of lead (Pb), zinc (Zn) and copper (Cu) mineralization in the dolomite rocks found in the southern part of the Zawarmala which is a well-known carbonate hosted Pb-Zn deposit occurring in the Zawar region in the south of Udaipur, Rajasthan, India.Remote sensing data analysis indicated the southern extension of the Zawarmala dolomite till Dhelai village where the outcrop truncates by the E-W fault.The carbonate with malachite strains supported by field evidences and petrological, ore microscopic, XRF and evidence of the presence of the trace elements reveal the presence of base metals in this area.The extension of occurrence of these dolomites rocks may be explored further for investigation of base metal mineralization in this region.
Designing a Conversational Agent as a Formative Course Evaluation Tool

Today’s graduating students face ever-changing environments when they enter their job life. Educational institutions must therefore continuously develop their course structure and content in order to prepare their students to be future employees. A very important means for developing the courses is the students’ course evaluations. Due to financial and organizational restrictions, these course evaluations are usually carried out quantitatively and at the end of the semester. However, past research has shown that this kind of evaluation faces certain constraints such as low acceptance rates, only time-related insights and low-quality answers that do not really help the lecturer to improve the course. Drawing on social response theory, we propose that conversational agents as a formative course evaluation tool are able to address the mentioned problems by interactively engaging with students. Therefore, we propose a set of design principles and evaluate them with our prototype Eva.
Distracted Driver Detection with Deep Convolutional Neural Network

According to the World Health Organization (WHO), over 1.3 million deaths occur worldwide each year due to traffic accidents alone. This figure elevates traffic mishaps to be the eight leading cause of death. According to another study the United States National Highway Traffic Safety Administration (NHTSA), the major cause of road deaths and injury is distracted drivers. Motivated by recent advancement of deep learning and computer vision in predicting drivers’ behaviour, this paper attempts to investigate the optimal deep learning network architecture to accurately detect distracted drivers over visual feed. Specifically, a thorough evaluation and detailed benchmark comparisons of pretrained deep convolutional neural network is carried out. Results indicate that the proposed VGG16network architecture is capable of achieving 96% accuracy on the test dataset images.
Semantic SPA framework for situational student-project allocation in education

Web 3.0 technologies have redefined the way classical problems in education are solved.The classical problems are constrained by interoperability challenges that can be addressed using semantic technologies.Student-project allocation (SPA) is one such classical problem in education, whose solution lies in finding a stable matching between student-project pairs.Although many methods exist to solve the SPA problem, recent developments in higher education, such as globalisation, has triggered interoperability challenge because of diversity in student and expert availability across disciplines, campuses, and universities.The author proposes the semantic SPA framework that addresses the interoperability challenge, by leveraging the existing semantic investments made by the educational institutions.The framework standardises the SPA data elements formally using semantic Web concepts, proposes reference ontology for the situational SPA problem, and adopts a semantic Web service approach, using OWL-S, for development.The OWL-S specification enables the automatic discovery, composition, and invocation of the Web services by educational Institutions or semantic agents on the Web, for situational SPA scenarios.
Breast Cancer Prediction using Classification Techniques

Now a day's most of woman affected Breast at life of different stages.This disease affected rate reduced year by year using different procedures of medical treatment.Among these processes, early identification of breast cancer generates good results.Classification methods can support to decrease negative decisions.Novel techniques such as like knowledge discovery in database have become well-liked research tool from medical research.With the help of these techniques extract the patterns and find out relationship between large numbers of objects.Based on historical datasets of corresponding cases stored in data bases, these results compare with fresh outcomes.In this paper, implement a new model based on classification techniques for analyzing breast cancer data.The statistical results show the effectiveness of techniques and compare these techniques based on accuracy, sensitivity, and specificity.
A brief review of blockchain-based DNS systems

One of the crucial parts of the internet is the domain name system, which works as a phonebook of the internet. The protocol is designed to be fast, reliable, and not shielded with a security mechanism, DNSSEC which adds authentication later. However, threats utilising DNS such as DoS/DDoS are increasing daily. On the other hand, blockchain-based DNS is secure by design. By reviewing and comparing it with the current DNS and its ecosystem, it is concluded that blockchain currently has challenges that need to be addressed before it can be adapted as a replacement for the existing DNS.
REFERS: refined and effective fuzzy e-commerce recommendation system

Online shopping culture is gaining traction globally and some of the biggest beneficiaries of this e-commerce shift are Amazon, eBay, etc. Recommendation systems guide online users in a personalised manner to choose what they want and their interest on each product present in the catalogue list. In such a scenario, the existing systems need complete information for making recommendations, which is not always possible in real applications. Therefore, a novel refined and effective fuzzy e-commerce recommendation system has been proposed in this paper that combines the benefits of difference in importance within the rating factors by a single user and new similarity measure approach that aims at improved recommendation list to the e-commerce user. The proposed methodology has been implemented using a new similarity measure on experimental datasets and the refined scores for such e-commerce website-based unlocked mobile phones are compared in this work against classic similarity measures.
A new hybrid gravitational particle swarm optimisation-ACO with local search mechanism, PSOGSA-ACO-Ls for TSP

The travelling salesman problem (TSP) is a hard combinatorial optimisation problem and a popular benchmarking problem at the same time. The TSP has also a number of practical real-world and industrial applications, such as routing in internet of things, IoT, networks, path planning in robotics and many others. In this paper, a new hybrid algorithm for the TSP is proposed; it combines gravitational particle swarm optimisation (PSOGSA) and ACO, and is called ant supervised by gravitational particle swarm optimisation with a local search, PSOGSA-ACO-LS. PSOGSA is used to optimise ACO settings while a local search mechanism, 2-Opt is employed by ACO to ameliorate its local solutions. The proposed method is evaluated using a set a test benches from the TSPLib database including: eil51, berlin52, st70, eil76, rat99, eil101, kroA100, and kroA200. Experimental results show that ACO-GPSO-LS is able to solve the set of TSP instances listed below including the large TSP data sets: kroA100, eli101 and kroA200.
Secure and Guarantee QoS in a Video Sequence: A New Approach Based on TLS Protocol to Secure Data and RTP to Ensure Real-Time Exchanges

Much of the Internet's communication is encrypted, and its content is only accessible at two endpoints, a client and a server. However, any encryption requires a key that must be negotiated without being revealed to potential attackers. The so-called TLS (Transport Layer Security) handshake is often used for this task without obviating that many fundamental parameters of TLS connections are transmitted explicitly. Thus, third parties have access to metadata, including information about the endpoints, how the connection is used. On the other hand, QoS is considered the central part of the communication used to judge the deliverable quality through several parameters (latency, jitter ...). This document describes a secure approach and meets mainly the requirements of quality of service on a communication channel (free, loaded, congested ...), using the robustness and flexibility of the TLS protocol represented on the characteristics of existing encryption keys on its list of "ciphers suites." We focused more particularly on the AES key (Advanced Encryption Standard), including the different sizes (128,192,256), given its resistance to various classical attacks (differential, linear, ...) and its lightness compared to other protocols such as DES, 3DES ... This method is useful in continuous communications in a time axis (video sequence, VOIP call...).
Cryptanalysis of Plantlet

Plantlet is a lightweight stream cipher designed by Mikhalev, Armknecht and Müller in IACR ToSC 2017. It has a Grain-like structure with two state registers of size 40 and 61 bits. In spite of this, the cipher does not seem to lose in security against generic Time-Memory-Data Tradeoff attacks due to the novelty of its design. The cipher uses a 80-bit secret key and a 90-bit IV. In this paper, we first present a key recovery attack on Plantlet that requires around 276.26 Plantlet encryptions. The attack leverages the fact that two internal states of Plantlet that differ in the 43rd LFSR location are guaranteed to produce keystream that are either equal or unequal in 45 locations with probability 1. Thus an attacker can with some probability guess that when 2 segments of keystream blocks possess the 45 bit difference just mentioned, they have been produced by two internal states that differ only in the 43rd LFSR location. Thereafter by solving a system of polynomial equations representing the keystream bits, the attacker can find the secret key if his guess was indeed correct, or reach some kind of contradiction if his guess was incorrect. In the latter event, he would repeat the procedure for other keystream blocks with the given difference. We show that the process when repeated a finite number of times, does indeed yield the value of the secret key.&#x0D; In the second part of the paper, we observe that the previous attack was limited to internal state differences that occurred at time instances that were congruent to 0 mod 80. We further observe that by generalizing the attack to include internal state differences that are congruent to all equivalence classed modulo 80, we lower the total number of keystream bits required to perform the attack and in the process reduce the attack complexity to 269.98 Plantlet encryptions.